This thesis aims to develop an automated system to detect flooded houses in images using deep learning and drone footage. The author collected over 3000 drone images of flooded areas and labeled them to identify houses and vegetation. The YOLOv7 deep learning model was trained on this dataset to accurately detect flooded buildings. Evaluation metrics like accuracy, precision, and recall showed the model could identify inundated houses and vegetation with 92% accuracy. The study demonstrates an effective approach for emergency response by quickly locating affected buildings.
The document describes a thesis submitted to fulfill the requirements for a bachelor's degree. The thesis aims to develop a deep learning model to detect houses in flooded areas using drone images. Specifically, it uses the YOLOv7 object detection model to automatically identify flooded buildings and vegetation from over 3,000 annotated images. The performance of the model is evaluated using various metrics like accuracy, precision, and confusion matrices. Additionally, the thesis presents an automated process for annotating images to speed up model training.
IRJET- A Real Time Yolo Human Detection in Flood Affected Areas based on Vide...IRJET Journal
This document proposes a method for real-time human detection in flood-affected areas using video content analysis and the YOLO object detection algorithm. It trains YOLO on the COCO Human dataset to detect and localize humans in video frames from surveillance cameras. The results show that YOLO can accurately detect multiple humans, even with occlusion, and single humans under varying illumination. This approach aims to help rescue operations quickly identify affected areas and prioritize aid.
This document is a project report on multiple object detection. It provides an introduction to the problem statement, applications, and challenges of object detection. It then reviews literature on object detection using neural networks. The introduction discusses image classification, localization, and object detection problems. It describes applications in face detection, autonomous driving, and surveillance. Challenges include variable output dimensions and requiring real-time performance while maintaining accuracy. The literature review discusses using deep learning for object detection and examines algorithms for a pedestrian counting system with affordable hardware.
ROAD POTHOLE DETECTION USING YOLOV4 DARKNETIRJET Journal
This document presents research on detecting potholes using the YOLOv4 object detection model in the Darknet framework. It begins with an introduction to the importance of road maintenance and automated pothole detection. It then describes the implementation process, which involves storing and preprocessing the dataset, training the YOLOv4 model, generating weights, and allowing users to upload images for detection. Case studies demonstrate the model successfully detecting potholes in test images. The document concludes that this method provides a cost-effective solution for government agencies to identify and repair potholes, improving road safety.
Complexity Neural Networks for Estimating Flood Process in Internet-of-Things...Dr. Amarjeet Singh
With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecastin gmodel based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyperparameters of the proposed CNN flood forecasting model.
Intelligent System For Face Mask DetectionIRJET Journal
This document presents research on developing an intelligent system to detect whether people are wearing face masks or not using deep learning techniques. The system uses a convolutional neural network called MobileNetV2 trained on a dataset of 480 masked and unmasked face images. Data augmentation is used to increase the size of the dataset. OpenCV is used for face detection. The system achieves 99% accuracy on the test dataset and can classify images and video frames in real-time. Applications discussed include use in airports, hospitals, offices and by law enforcement to monitor compliance with mask mandates and prevent the spread of COVID-19.
IRJET - An Intelligent Pothole Detection System using Deep LearningIRJET Journal
This document describes a proposed intelligent pothole detection system using deep learning. The system would use a convolutional neural network trained on pothole image data to detect potholes in photos taken from a vehicle mounted camera. When potholes are detected, their locations would be stored in a cloud database. A mobile app would allow users to view the locations of detected potholes on a map. This would help automate pothole detection to assist road maintenance authorities and provide drivers with pothole location information. The proposed system aims to address the inefficiencies of manual pothole detection by automating the process using deep learning and cloud/mobile technologies.
The document describes a thesis submitted to fulfill the requirements for a bachelor's degree. The thesis aims to develop a deep learning model to detect houses in flooded areas using drone images. Specifically, it uses the YOLOv7 object detection model to automatically identify flooded buildings and vegetation from over 3,000 annotated images. The performance of the model is evaluated using various metrics like accuracy, precision, and confusion matrices. Additionally, the thesis presents an automated process for annotating images to speed up model training.
IRJET- A Real Time Yolo Human Detection in Flood Affected Areas based on Vide...IRJET Journal
This document proposes a method for real-time human detection in flood-affected areas using video content analysis and the YOLO object detection algorithm. It trains YOLO on the COCO Human dataset to detect and localize humans in video frames from surveillance cameras. The results show that YOLO can accurately detect multiple humans, even with occlusion, and single humans under varying illumination. This approach aims to help rescue operations quickly identify affected areas and prioritize aid.
This document is a project report on multiple object detection. It provides an introduction to the problem statement, applications, and challenges of object detection. It then reviews literature on object detection using neural networks. The introduction discusses image classification, localization, and object detection problems. It describes applications in face detection, autonomous driving, and surveillance. Challenges include variable output dimensions and requiring real-time performance while maintaining accuracy. The literature review discusses using deep learning for object detection and examines algorithms for a pedestrian counting system with affordable hardware.
ROAD POTHOLE DETECTION USING YOLOV4 DARKNETIRJET Journal
This document presents research on detecting potholes using the YOLOv4 object detection model in the Darknet framework. It begins with an introduction to the importance of road maintenance and automated pothole detection. It then describes the implementation process, which involves storing and preprocessing the dataset, training the YOLOv4 model, generating weights, and allowing users to upload images for detection. Case studies demonstrate the model successfully detecting potholes in test images. The document concludes that this method provides a cost-effective solution for government agencies to identify and repair potholes, improving road safety.
Complexity Neural Networks for Estimating Flood Process in Internet-of-Things...Dr. Amarjeet Singh
With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecastin gmodel based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyperparameters of the proposed CNN flood forecasting model.
Intelligent System For Face Mask DetectionIRJET Journal
This document presents research on developing an intelligent system to detect whether people are wearing face masks or not using deep learning techniques. The system uses a convolutional neural network called MobileNetV2 trained on a dataset of 480 masked and unmasked face images. Data augmentation is used to increase the size of the dataset. OpenCV is used for face detection. The system achieves 99% accuracy on the test dataset and can classify images and video frames in real-time. Applications discussed include use in airports, hospitals, offices and by law enforcement to monitor compliance with mask mandates and prevent the spread of COVID-19.
IRJET - An Intelligent Pothole Detection System using Deep LearningIRJET Journal
This document describes a proposed intelligent pothole detection system using deep learning. The system would use a convolutional neural network trained on pothole image data to detect potholes in photos taken from a vehicle mounted camera. When potholes are detected, their locations would be stored in a cloud database. A mobile app would allow users to view the locations of detected potholes on a map. This would help automate pothole detection to assist road maintenance authorities and provide drivers with pothole location information. The proposed system aims to address the inefficiencies of manual pothole detection by automating the process using deep learning and cloud/mobile technologies.
INDOOR AND OUTDOOR NAVIGATION ASSISTANCE SYSTEM FOR VISUALLY IMPAIRED PEOPLE ...IRJET Journal
This document describes a proposed system to assist visually impaired individuals using object detection. The system uses YOLO (You Only Look Once) deep learning for fast and reliable object detection in images captured by a webcam in real-time. Detected objects are identified and conveyed to the user via text-to-speech. This allows visually impaired users to navigate indoor and outdoor environments with information about surrounding objects. The proposed system aims to address challenges faced by existing assistive technologies through improved accuracy and real-time performance of object recognition compared to other methods.
Intrusion Detection and Prevention System in an Enterprise NetworkOkehie Collins
This document describes a project on intrusion detection and prevention systems in an enterprise network. It was submitted by Okehie Collins Obinna to the Department of Computer Science at the Federal University of Technology in partial fulfillment of a Bachelor of Technology degree in Computer Science. The project analyzes intrusion detection and prevention technologies used in enterprise networks and designs a desktop application to monitor a computer network system for possible intrusions and provide an interface for a network administrator.
This document discusses object identification using convolutional neural networks and the YOLO detection algorithm. It begins with an introduction to neural networks and their history. It then discusses datasets used to train object detection models. The document describes experiments conducted using the YOLO detector on different sized images to evaluate performance. Processing speed and objects detected were compared between the CPU and GPU. The YOLO detector was then tested on a set of 500 images, and its performance metrics were reported.
Development of durian leaf disease detection on Android device IJECEIAES
Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponent’s objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf.
Voice Enable Blind Assistance System -Real time Object DetectionIRJET Journal
This document describes a voice-enabled blind assistance system using real-time object detection. The system uses a Single Shot Multi-Box Detection (SSD) model with MobileNet to detect objects in frames captured by a webcam in real-time. When an object is detected, its class is converted to speech using text-to-speech and provided to blind users along with alerts if the object is too close. The system aims to help visually impaired people gain more independence by identifying objects and hazards in their environment. An experiment showed the SSD MobileNet model achieved accurate real-time detection of household objects.
Monitoring Students Using Different Recognition Techniques for Surveilliance ...IRJET Journal
This document discusses using computer vision techniques like convolutional neural networks to monitor students and enforce dress codes in educational institutions. It proposes a system using cameras and image processing to identify whether students are properly dressed according to the dress code. The system would classify images of students as either following or not following the dress code. It also discusses related work on using technologies like biometrics and RFID cards for automated student attendance tracking and implications for security and discipline in schools.
A Survey on Solar Based Smart Antibiotic Sprinkler System Using Internet of T...IRJET Journal
1. The document describes a solar-powered smart sprinkler system using IoT that monitors soil moisture levels and controls watering remotely.
2. It uses a soil moisture sensor connected to a sprinkler and WiFi module to automate watering based on moisture readings. An app allows remote monitoring and control of the system.
3. The system aims to reduce overuse of water and fertilizers for agriculture by precisely watering only when needed, lowering costs and environmental impact compared to traditional sprinklers.
IRJET- Identification of Missing Person in the Crowd using Pretrained Neu...IRJET Journal
The document describes a proposed system to identify missing persons in crowded areas using pretrained convolutional neural networks. The system would involve collecting images of missing persons from different angles to create a dataset for training. An AlexNet pretrained neural network would then be used to detect faces in live video captured by a drone camera of crowded areas. Detected faces would be cropped, stored in a database, and used to further train the network. During testing, the system could identify missing persons by displaying their images when detected in the crowd. The goal of the system is to help police efficiently locate missing people in crowded public settings like festivals or meetings.
Acoustic event characterization for service robot using convolutional networksIJECEIAES
This paper presents and discusses the creation of a sound event classification model using deep learning. In the design of service robots, it is necessary to include routines that improve the response of both the robot and the human being throughout the interaction. These types of tasks are critical when the robot is taking care of children, the elderly, or people in vulnerable situations. Certain dangerous situations are difficult to identify and assess by an autonomous system, and yet, the life of the users may depend on these robots. Acoustic signals correspond to events that can be detected at a great distance, are usually present in risky situations, and can be continuously sensed without incurring privacy risks. For the creation of the model, a customized database is structured with seven categories that allow to categorize a problem, and eventually allow the robot to provide the necessary help. These audio signals are processed to produce graphical representations consistent with human acoustic identification. These images are then used to train three convolutional models identified as high-performing in this type of problem. The three models are evaluated with specific metrics to identify the best-performing model. Finally, the results of this evaluation are discussed and analyzed.
This document discusses using artificial intelligence and satellite imagery to identify natural disasters. It proposes comparing pixel values in bi-temporal (two-time period) satellite images from before and after a disaster to detect changes indicating damage. A change detection model would analyze satellite images captured over time of a specific area to identify variability indicating a disaster occurrence. Deep learning models could then be trained on these change maps to automatically detect and classify disaster types and affected areas for faster disaster assessment and relief coordination.
This document summarizes a thesis presentation on an autonomous robotic system for nondestructive evaluation of asphalt pavement using deep learning. The system uses a robot equipped with vision sensors and an impact echo sensor. Deep learning models are used to detect cracks from images and classify crack severity from impact echo signals. The robot can autonomously collect data, perform real-time crack detection using onboard processing, and present severity maps quantifying the cracks. The system provides a low-cost way to inspect roads and quantify cracking issues. Future work could improve low-light crack detection, evaluate subsurface conditions, and integrate additional sensors to cover more area faster.
A Survey on Person Detection for Social Distancing and Safety Violation Alert...IRJET Journal
This document discusses methods for monitoring social distancing using video surveillance and deep learning techniques. It describes how faster R-CNN, single shot detector (SSD) and YOLO v3 deep learning models can be used to detect people in video frames and calculate the distance between individuals to determine if social distancing guidelines are being followed. If distances between people are found to be unsafe, the system can send alerts or cautions. The methodology is intended to help prevent the spread of COVID-19 by monitoring adherence to social distancing and triggering warnings if safety violations are detected.
11.0003www.iiste.org call for paper.survey on wireless intelligent video surv...Alexander Decker
This document summarizes a survey on wireless intelligent video surveillance systems using moving object recognition technology. It describes the typical components and processes involved, including capturing video, creating a background template, detecting moving objects via background subtraction, and sending the separated moving object to a destination. It also discusses techniques for motion detection, such as frame differencing, background subtraction, and optical flow. The system architecture involves a camera capturing video, subtracting frames to identify moving objects, and transmitting just the moving objects.
3.survey on wireless intelligent video surveillance system using moving objec...Alexander Decker
This document summarizes a survey on wireless intelligent video surveillance systems using moving object recognition technology. It describes the typical components and processing steps of such systems, including motion detection using background subtraction, object tracking, and transmitting detected objects wirelessly. The document also reviews the evolution of video surveillance technologies from analog CCTV to modern wireless intelligent systems and discusses design considerations like video quality and compression.
This document describes a study that uses machine learning algorithms to analyze flood data and predict flood impacts. The study collected flood data from various states in India containing information on start/end dates, duration, causes, affected districts/states, and casualties including human injuries and deaths as well as animal fatalities. Various machine learning models like decision trees, random forests, SVMs, and neural networks were trained on the data. The models' performance was evaluated based on metrics like accuracy, precision, recall, and F1-score. The results showed that some states experienced higher numbers of human/animal casualties from floods compared to others. Graphs and charts were used to analyze relationships between variables in the data and compare flood impacts like casualties and
YOLO BASED SHIP IMAGE DETECTION AND CLASSIFICATIONIRJET Journal
This document presents a method for ship image detection and classification using YOLO and CNNs. It proposes using a CNN to extract features from input ship images, which are then fed into an SVM classifier to improve classification performance over a standard CNN. The method achieved 98% accuracy. It discusses applying deep learning techniques like CNNs to overcome limitations of traditional machine learning for complex computer vision tasks using image data. The document also provides background on deep learning, CNNs, neural networks, and challenges in ship detection from remote sensing images.
Design and Development of Keen Kid Wallow Salvage Frameworkijtsrd
In India for recent days people are confronting a troubled remorseless circumstance like Childs have fell in the drag well and struck in the opening which is uncovered and getting caught. Salvage of caught kid from bore well is exceptionally dangerous and troublesome cycle when contrasted with other accidents. It takes more than a day to save the child. In this undertaking the plan and advancement of shrewd kid bore well recue framework. Initially when children are coming in front of bore well then I.R sensor 1 is identified and a bell will give sign. At the point when the kid fall in the drag well then I.R sensor 2 will be distinguished and naturally the net will lift up the kid and saves from water without plunging. Similarly a SMS will ship off the relating individual from the road. Subsequently this undertaking gives compelling outcome. Mr. Maddirala. Ajay Kumar | Mrs. D. Santhipriya "Design and Development of Keen Kid Wallow Salvage Framework" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47483.pdf Paper URL : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/47483/design-and-development-of-keen-kid-wallow-salvage-framework/mr-maddirala-ajay-kumar
Low resource deep learning to detect waste intensity in the river flowjournalBEEI
This document summarizes a study that used the YOLO v3 deep learning algorithm to detect waste in river flows and calculate waste intensity. Researchers collected 340 images of waste in rivers to use as a training dataset for a YOLO v3 model. The model was tested on videos of river flows and achieved 98.74% confidence in detecting waste objects. The resulting application can detect a variety of waste sizes, from small food wrappers to large objects, and count the number of waste items intersecting a drawn reference line in each video frame. This waste detection and counting system aims to help monitor waste in rivers and predict when river cleaning is needed.
The document describes a blind assistance system called Sanjaya that uses object detection and depth estimation to help visually impaired individuals navigate environments. The system uses a SSD MobileNet model trained on the COCO dataset via TensorFlow's object detection API to identify objects in camera images in real-time. It then uses depth estimation to calculate distances and provides voice feedback alerts to users about detected objects and their proximity. The system aims to allow visually impaired people to have improved comprehension of their surroundings and navigation abilities.
INDOOR AND OUTDOOR NAVIGATION ASSISTANCE SYSTEM FOR VISUALLY IMPAIRED PEOPLE ...IRJET Journal
This document describes a proposed system to assist visually impaired individuals using object detection. The system uses YOLO (You Only Look Once) deep learning for fast and reliable object detection in images captured by a webcam in real-time. Detected objects are identified and conveyed to the user via text-to-speech. This allows visually impaired users to navigate indoor and outdoor environments with information about surrounding objects. The proposed system aims to address challenges faced by existing assistive technologies through improved accuracy and real-time performance of object recognition compared to other methods.
Intrusion Detection and Prevention System in an Enterprise NetworkOkehie Collins
This document describes a project on intrusion detection and prevention systems in an enterprise network. It was submitted by Okehie Collins Obinna to the Department of Computer Science at the Federal University of Technology in partial fulfillment of a Bachelor of Technology degree in Computer Science. The project analyzes intrusion detection and prevention technologies used in enterprise networks and designs a desktop application to monitor a computer network system for possible intrusions and provide an interface for a network administrator.
This document discusses object identification using convolutional neural networks and the YOLO detection algorithm. It begins with an introduction to neural networks and their history. It then discusses datasets used to train object detection models. The document describes experiments conducted using the YOLO detector on different sized images to evaluate performance. Processing speed and objects detected were compared between the CPU and GPU. The YOLO detector was then tested on a set of 500 images, and its performance metrics were reported.
Development of durian leaf disease detection on Android device IJECEIAES
Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponent’s objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf.
Voice Enable Blind Assistance System -Real time Object DetectionIRJET Journal
This document describes a voice-enabled blind assistance system using real-time object detection. The system uses a Single Shot Multi-Box Detection (SSD) model with MobileNet to detect objects in frames captured by a webcam in real-time. When an object is detected, its class is converted to speech using text-to-speech and provided to blind users along with alerts if the object is too close. The system aims to help visually impaired people gain more independence by identifying objects and hazards in their environment. An experiment showed the SSD MobileNet model achieved accurate real-time detection of household objects.
Monitoring Students Using Different Recognition Techniques for Surveilliance ...IRJET Journal
This document discusses using computer vision techniques like convolutional neural networks to monitor students and enforce dress codes in educational institutions. It proposes a system using cameras and image processing to identify whether students are properly dressed according to the dress code. The system would classify images of students as either following or not following the dress code. It also discusses related work on using technologies like biometrics and RFID cards for automated student attendance tracking and implications for security and discipline in schools.
A Survey on Solar Based Smart Antibiotic Sprinkler System Using Internet of T...IRJET Journal
1. The document describes a solar-powered smart sprinkler system using IoT that monitors soil moisture levels and controls watering remotely.
2. It uses a soil moisture sensor connected to a sprinkler and WiFi module to automate watering based on moisture readings. An app allows remote monitoring and control of the system.
3. The system aims to reduce overuse of water and fertilizers for agriculture by precisely watering only when needed, lowering costs and environmental impact compared to traditional sprinklers.
IRJET- Identification of Missing Person in the Crowd using Pretrained Neu...IRJET Journal
The document describes a proposed system to identify missing persons in crowded areas using pretrained convolutional neural networks. The system would involve collecting images of missing persons from different angles to create a dataset for training. An AlexNet pretrained neural network would then be used to detect faces in live video captured by a drone camera of crowded areas. Detected faces would be cropped, stored in a database, and used to further train the network. During testing, the system could identify missing persons by displaying their images when detected in the crowd. The goal of the system is to help police efficiently locate missing people in crowded public settings like festivals or meetings.
Acoustic event characterization for service robot using convolutional networksIJECEIAES
This paper presents and discusses the creation of a sound event classification model using deep learning. In the design of service robots, it is necessary to include routines that improve the response of both the robot and the human being throughout the interaction. These types of tasks are critical when the robot is taking care of children, the elderly, or people in vulnerable situations. Certain dangerous situations are difficult to identify and assess by an autonomous system, and yet, the life of the users may depend on these robots. Acoustic signals correspond to events that can be detected at a great distance, are usually present in risky situations, and can be continuously sensed without incurring privacy risks. For the creation of the model, a customized database is structured with seven categories that allow to categorize a problem, and eventually allow the robot to provide the necessary help. These audio signals are processed to produce graphical representations consistent with human acoustic identification. These images are then used to train three convolutional models identified as high-performing in this type of problem. The three models are evaluated with specific metrics to identify the best-performing model. Finally, the results of this evaluation are discussed and analyzed.
This document discusses using artificial intelligence and satellite imagery to identify natural disasters. It proposes comparing pixel values in bi-temporal (two-time period) satellite images from before and after a disaster to detect changes indicating damage. A change detection model would analyze satellite images captured over time of a specific area to identify variability indicating a disaster occurrence. Deep learning models could then be trained on these change maps to automatically detect and classify disaster types and affected areas for faster disaster assessment and relief coordination.
This document summarizes a thesis presentation on an autonomous robotic system for nondestructive evaluation of asphalt pavement using deep learning. The system uses a robot equipped with vision sensors and an impact echo sensor. Deep learning models are used to detect cracks from images and classify crack severity from impact echo signals. The robot can autonomously collect data, perform real-time crack detection using onboard processing, and present severity maps quantifying the cracks. The system provides a low-cost way to inspect roads and quantify cracking issues. Future work could improve low-light crack detection, evaluate subsurface conditions, and integrate additional sensors to cover more area faster.
A Survey on Person Detection for Social Distancing and Safety Violation Alert...IRJET Journal
This document discusses methods for monitoring social distancing using video surveillance and deep learning techniques. It describes how faster R-CNN, single shot detector (SSD) and YOLO v3 deep learning models can be used to detect people in video frames and calculate the distance between individuals to determine if social distancing guidelines are being followed. If distances between people are found to be unsafe, the system can send alerts or cautions. The methodology is intended to help prevent the spread of COVID-19 by monitoring adherence to social distancing and triggering warnings if safety violations are detected.
11.0003www.iiste.org call for paper.survey on wireless intelligent video surv...Alexander Decker
This document summarizes a survey on wireless intelligent video surveillance systems using moving object recognition technology. It describes the typical components and processes involved, including capturing video, creating a background template, detecting moving objects via background subtraction, and sending the separated moving object to a destination. It also discusses techniques for motion detection, such as frame differencing, background subtraction, and optical flow. The system architecture involves a camera capturing video, subtracting frames to identify moving objects, and transmitting just the moving objects.
3.survey on wireless intelligent video surveillance system using moving objec...Alexander Decker
This document summarizes a survey on wireless intelligent video surveillance systems using moving object recognition technology. It describes the typical components and processing steps of such systems, including motion detection using background subtraction, object tracking, and transmitting detected objects wirelessly. The document also reviews the evolution of video surveillance technologies from analog CCTV to modern wireless intelligent systems and discusses design considerations like video quality and compression.
This document describes a study that uses machine learning algorithms to analyze flood data and predict flood impacts. The study collected flood data from various states in India containing information on start/end dates, duration, causes, affected districts/states, and casualties including human injuries and deaths as well as animal fatalities. Various machine learning models like decision trees, random forests, SVMs, and neural networks were trained on the data. The models' performance was evaluated based on metrics like accuracy, precision, recall, and F1-score. The results showed that some states experienced higher numbers of human/animal casualties from floods compared to others. Graphs and charts were used to analyze relationships between variables in the data and compare flood impacts like casualties and
YOLO BASED SHIP IMAGE DETECTION AND CLASSIFICATIONIRJET Journal
This document presents a method for ship image detection and classification using YOLO and CNNs. It proposes using a CNN to extract features from input ship images, which are then fed into an SVM classifier to improve classification performance over a standard CNN. The method achieved 98% accuracy. It discusses applying deep learning techniques like CNNs to overcome limitations of traditional machine learning for complex computer vision tasks using image data. The document also provides background on deep learning, CNNs, neural networks, and challenges in ship detection from remote sensing images.
Design and Development of Keen Kid Wallow Salvage Frameworkijtsrd
In India for recent days people are confronting a troubled remorseless circumstance like Childs have fell in the drag well and struck in the opening which is uncovered and getting caught. Salvage of caught kid from bore well is exceptionally dangerous and troublesome cycle when contrasted with other accidents. It takes more than a day to save the child. In this undertaking the plan and advancement of shrewd kid bore well recue framework. Initially when children are coming in front of bore well then I.R sensor 1 is identified and a bell will give sign. At the point when the kid fall in the drag well then I.R sensor 2 will be distinguished and naturally the net will lift up the kid and saves from water without plunging. Similarly a SMS will ship off the relating individual from the road. Subsequently this undertaking gives compelling outcome. Mr. Maddirala. Ajay Kumar | Mrs. D. Santhipriya "Design and Development of Keen Kid Wallow Salvage Framework" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47483.pdf Paper URL : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/47483/design-and-development-of-keen-kid-wallow-salvage-framework/mr-maddirala-ajay-kumar
Low resource deep learning to detect waste intensity in the river flowjournalBEEI
This document summarizes a study that used the YOLO v3 deep learning algorithm to detect waste in river flows and calculate waste intensity. Researchers collected 340 images of waste in rivers to use as a training dataset for a YOLO v3 model. The model was tested on videos of river flows and achieved 98.74% confidence in detecting waste objects. The resulting application can detect a variety of waste sizes, from small food wrappers to large objects, and count the number of waste items intersecting a drawn reference line in each video frame. This waste detection and counting system aims to help monitor waste in rivers and predict when river cleaning is needed.
The document describes a blind assistance system called Sanjaya that uses object detection and depth estimation to help visually impaired individuals navigate environments. The system uses a SSD MobileNet model trained on the COCO dataset via TensorFlow's object detection API to identify objects in camera images in real-time. It then uses depth estimation to calculate distances and provides voice feedback alerts to users about detected objects and their proximity. The system aims to allow visually impaired people to have improved comprehension of their surroundings and navigation abilities.
International Upcycling Research Network advisory board meeting 4Kyungeun Sung
Slides used for the International Upcycling Research Network advisory board 4 (last one). The project is based at De Montfort University in Leicester, UK, and funded by the Arts and Humanities Research Council.
ARENA - Young adults in the workplace (Knight Moves).pdfKnight Moves
Presentations of Bavo Raeymaekers (Project lead youth unemployment at the City of Antwerp), Suzan Martens (Service designer at Knight Moves) and Adriaan De Keersmaeker (Community manager at Talk to C)
during the 'Arena • Young adults in the workplace' conference hosted by Knight Moves.
Explore the essential graphic design tools and software that can elevate your creative projects. Discover industry favorites and innovative solutions for stunning design results.
Architectural and constructions management experience since 2003 including 18 years located in UAE.
Coordinate and oversee all technical activities relating to architectural and construction projects,
including directing the design team, reviewing drafts and computer models, and approving design
changes.
Organize and typically develop, and review building plans, ensuring that a project meets all safety and
environmental standards.
Prepare feasibility studies, construction contracts, and tender documents with specifications and
tender analyses.
Consulting with clients, work on formulating equipment and labor cost estimates, ensuring a project
meets environmental, safety, structural, zoning, and aesthetic standards.
Monitoring the progress of a project to assess whether or not it is in compliance with building plans
and project deadlines.
Attention to detail, exceptional time management, and strong problem-solving and communication
skills are required for this role.
1. DETECTING HOUSES IN FLOODED
AREA WITH THE HELP OF DRONE
IMAGE USING DEEP LEARNING
Submitted By
Mohammad Izaz Ahamed
CSE 01706540
Under The Supervision Of
Mr. Sowmitra Das
Assistant Professor
This thesis is submitted to the Department of Computer Science and Engineering of
Port City International University in the fulfillment of the requirements for the degree
of Bachelor of Science (Engineering)
Department of Computer Science & Engineering
Port City International University
7-14, Nikunja Housing Society, South khulshi, Chattogram, Bangladesh
January 2023
2. DETECTING HOUSES IN FLOODED AREA WITH THE HELP OF DRONE IMAGE USING DEEP LEARNING
DETECTING HOUSES IN FLOODED
AREA WITH THE HELP OF DRONE
IMAGE USING DEEP LEARNING
Submitted By
Mohammad Izaz Ahamed
CSE 01706540
Under The Supervision Of
Mr. Sowmitra Das
Assistant Professor
Department of Computer Science & Engineering
Port City International University
7-14, Nikunja Housing Society, South khulshi, Chattogram, Bangladesh
This thesis is submitted to the Department of Computer Science and Engineering of
Port City International University in the fulfillment of the requirements for the degree
of Bachelor of Science (Engineering)
January 2023
3. Ⅰ
DETECTING HOUSES IN FLOODED AREA WITH THE HELP OF DRONE IMAGE USING DEEP LEARNING
DECLARATION
It's hereby declared that I have independently completed this thesis under the supervision of
Mr. Sowmitra Das, Assistant Professor of the Department of CSE at Port City International
University. To the best of my knowledge, no portion of this work has been previously
submitted for any other degree or qualification at this or any other educational institution. I
also confirm that I have only used the resources that were specifically authorized.
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
(Signature of the candidate)
Mohammad Izaz Ahamed
CSE 01706540
CSE 17 (day)
Department of CSE
Port City International University
4. Ⅱ
DETECTING HOUSES IN FLOODED AREA WITH THE HELP OF DRONE IMAGE USING DEEP LEARNING
APPROVAL
This thesis titled “DETECTING HOUSES IN FLOODED AREA WITH THE HELP OF
DRONE IMAGE USING DEEP LEARNING”, by Mohammad Izaz Ahamed has been
approved for summation to the Department of Computer Science and Engineering, Port City
International University, in partial fulfillment of the requirement for the degree of Bachelor
of Science (Engineering).
_ _ _ _ _ _ _ _ _ _ _ _ _ _
(Signature of Supervisor)
Mr. Sowmitra Das
Assistant Professor,
Department of Computer Science and Engineering
Port City International University
7, Nikunja Housing Society, South Khushi, Chottogram- 4202.
Chittagong, Bangladesh.
Email: sowmitracsecu@gmail.com
Telephone: +8801620325472
5. Ⅲ
DETECTING HOUSES IN FLOODED AREA WITH THE HELP OF DRONE IMAGE USING DEEP LEARNING
DEDICATION
This thesis is respectfully dedicated to my esteemed teachers, my loving parents, and all those
who have supported and encouraged me throughout my academic journey and specially our
respected supervisor Mr. Sowmitra Das.
6. Ⅳ
DETECTING HOUSES IN FLOODED AREA WITH THE HELP OF DRONE IMAGE USING DEEP LEARNING
ACKNOWLEDGEMENT
I would like to begin by offering my sincerest gratitude to the Almighty Allah for providing
me with the strength, determination, and opportunity to complete this project on time. My
deepest appreciation goes to my supervisor, Mr. Sowmitra Das, for his invaluable guidance
and support throughout the duration of this project. I am also grateful to my other esteemed
teachers at my university for their advice and assistance, both directly and indirectly, in
helping me stay focused on my thesis. Lastly, I extend my heartfelt thanks to my friends for
their unwavering support.
7. Ⅴ
DETECTING HOUSES IN FLOODED AREA WITH THE HELP OF DRONE IMAGE USING DEEP LEARNING
ABSTRACT
Floods are a major natural disaster that can cause extensive damage to property,
infrastructure, and result in significant economic losses annually. In order to effectively
respond to such disasters, there is a need to develop an approach that can quickly detect the
houses in flooded areas. Satellite remote sensing has been utilized in emergency responses,
but it has limitations such as long revisit periods and inability to operate during rainy or
cloudy weather conditions. To address these limitations, this study proposes the use of drones
to detect flooded buildings. Through the utilization of deep learning models, specifically
YOLOv7, this study aims to develop an automated detection system for flooded buildings
using drone images. The results of the study show that the inundation of buildings and
vegetation can be accurately detected from the images with 92% accuracy. The performance
of the developed system was evaluated using various metrics such as accuracy, precision,
recall, and confusion matrices. Additionally, this study also presents an automated annotation
process to speed up the process of image annotation.
8. DETECTING HOUSES IN FLOODED AREA WITH THE HELP OF DRONE IMAGE USING DEEP LEARNING
TABLE OF CONTENTS
DECLARATION Ⅰ
APPROVAL Ⅱ
DEDICATION Ⅲ
ACKNOWLEDGMENTS Ⅳ
ABSTRACT Ⅴ
CHAPTER 1 1
INTRODUCTION 1
1.1 Overview 1
1.2 Problem Statement 1
1.3 Motivation 2
1.4 Objective 2
1.5 Object Detection 2
1.5.1 Object Localization 3
1.5.2 Object Classification 3
1.5.3 Object Instance Segmentation 3
1.6 YOLOv7 3
1.7 Organization of the document 4
CHAPTER 2 6
LITERATURE REVIEW 6
2.1 Overview 6
2.2 Previous Work 6
2.3 Research Summary 8
2.4 Scope of this problem 8
2.5 Challenges 8
CHAPTER 3 9
METHODOLOGY 9
3.1 Working Procedure 9
3.2 Proposed System 9
3.3 Data Collection 10
3.4 Data Preprocessing 11
3.4.1 Removing Unnecessary data 11
3.4.2 Data Resizing 11
3.5 Image Annotation 11
3.6 YOLOv7 Image Annotation 12
3.7 Automated Image Annotation 13
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CHAPTER 4 16
HARDWARE AND TOOLKIT 16
4.1 Tools 16
4.1.1 Python 16
4.1.2 NumPy 17
4.1.3 Pandas 17
4.1.4 OS Module 17
4.1.5 Matplotlib 18
4.1.6 OpenCV 18
4.1.7 VS Code 18
4.1.8 Colab Notebook 19
4.2 Hardware 19
CHAPTER 5 20
RESULT & DISCUSSION 20
5.1 Performance Evaluation 20
5.1.1 IOU 20
5.1.2 Precision and Recall 21
5.1.3 Average Precision 21
5.1.4 Mean Average Precision 21
5.2 Experimental Analysis 22
5.3 YOLOv7 On Manually Annotated Dataset 22
5.3.1 Object Detection Report 22
5.3.2 Confusion Matrix 23
5.3.3 Accuracy & Loss Curve 23
5.4 YOLOv7 On Automatic Annotated Dataset 24
5.4.1 Object Detection Report 24
5.4.2 Confusion Matrix 24
5.4.3 Accuracy & Loss Curve 25
5.5 Comparison of Performance 26
CHAPTER 6 27
CONCLUSION & FUTURE WORK 27
6.1 Conclusion 27
6.2 Limitation 27
6.3 Future Work 27
CHAPTER 7 28
REFERENCES 28
7.1 References 28
10. DETECTING HOUSES IN FLOODED AREA WITH THE HELP OF DRONE IMAGE USING DEEP LEARNING
TABLE OF FIGURES
Figure 1: Basic flowchart of the proposed system 9
Figure 2: Example dataset Images of Flooded houses 10
Figure 3: YOLOv7 Annotation Format 12
Figure 4: YOLOv7 Annotated .txt File 12
Figure 5: Converting the image to Grayscale 13
Figure 6: Apply Adaptive Canny Edge Detection 14
Figure 7: Apply Dilation & Erosion 14
Figure 8: Find Coordinates 15
Figure 9: IOU 20
Figure 10: Object Detection Report For Manually Annotated Data 22
Figure 11: Confusion Matrix For Manually Annotated Data 23
Figure 12: Accuracy & Loss Curve For Manually Annotated Data 23
Figure 13: Object Detection Report For Automatic Annotated Data 24
Figure 14: Confusion Matrix For Automatic Annotated Data 24
Figure 15: Accuracy & Loss Curve For Automatic Annotated Data 25
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CHAPTER 1
INTRODUCTION
1.1 Overview
Floods are one of the major natural disasters that cause huge damage to property,
infrastructure and economic losses every year. There is a need to develop an approach that
could instantly detect the houses in the flooded area. House detection using drones can be
helpful in a variety of ways, especially in emergency response situations such as floods. By
quickly and accurately identifying flooded buildings, emergency responders can prioritize
and target their efforts to provide aid and assistance to the most affected areas. Additionally,
the information obtained from house detection can also be used for post-disaster damage
assessments and to inform rebuilding efforts. Furthermore, this information can also be used
for urban planning and land management purposes, for example, to better understand the
distribution of housing in a region, and to identify areas that may be vulnerable to flooding.
In summary, house detection can provide valuable information to aid in emergency response
and recovery efforts, as well as for urban planning and land management. The objective of
our research is to develop a method for detecting houses in flooded areas. To achieve this, we
have used a dataset of over 3000 images (after augmentation) of flooded houses. To train our
model, we labeled the images in our dataset both manually and automatically by identifying
the presence of buildings and vegetation. Using the YOLOv7 model, we aim to develop an
automated detection system that can accurately identify flooded houses in images.
1.2 Problem Statement
The goal of this research is to create a system for identifying houses in flooded areas using
deep learning techniques. The proposed method utilizes the YOLOv7 model for automated
detection of flooded houses in images, with the aim of providing accurate and efficient
detection results.
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1.3 Motivation
The motivation for this research stems from the need to address the limitations of current
methods for detecting flooded houses. The traditional approaches such as satellite remote
sensing have long revisit periods and are unable to operate during adverse weather
conditions. This research aims to provide a more efficient and accurate method for
identifying flooded houses using deep learning techniques and drone images. The proposed
method is expected to aid emergency response efforts, inform rebuilding efforts and urban
planning, as well as make a meaningful impact on society by contributing to the development
of a tool that can assist in disaster response and recovery. Furthermore, this research presents
an opportunity to explore and contribute to the field of computer vision and deep learning.
1.4 Objective
The objective of this research is to develop a method for identifying and detecting impacted
houses in flooded areas using deep learning-based object detection techniques. Specifically,
our aim is to utilize the YOLOv7 model to detect and identify houses in images of flooded
areas with high accuracy, and to create an automatic annotation system to efficiently label
and process the dataset used to train our object detection model.
1.5 Object Detection
Object recognition is a computational technique related to computer vision and image
processing that deals with recognizing instances of semantic objects of a particular class
(people, buildings, cars, fruits, etc.) in digital images or videos. One popular approach to
object detection is using the YOLO (You Only Look Once) algorithm, which is a real-time
object detection system that is able to effectively detect objects in images and videos using a
single pass of the convolutional neural network (CNN). YOLO divides the input image into a
grid of cells and for each cell, it predicts a set of bounding boxes and corresponding class
probabilities. This approach allows for fast and accurate object detection in real-time
applications. Object recognition is a computer vision task that includes several subtasks such
as object localization, object classification, and object instance segmentation.
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1.5.1 Object Localization
This task is to determine the location of an object within an image or video. The most
common representation of the location of an object is a bounding box, which is a rectangular
box that surrounds an object. A bounding box is defined by the coordinates of the upper left
corner of the box, its width, and its height. This task is critical for object detection, as it
provides the information needed to identify the presence of an object within an image or
video.
1.5.2 Object Classification
This task is to identify the class or category of an object within an image or video. It is a
supervised machine learning problem, where the model is trained on a dataset of labeled
images and learns to recognize the different object classes. Object classification is used to
distinguish between different object classes, and the output of this task is typically a
probability score for each class.
1.5.3 Object Instance Segmentation
This task is to identify and segment out specific instances of objects within an image or
video. It is an extension of object localization, where not only the location of an object is
identified, but also the object pixels are segmented out. This task is more complex than object
localization and classification, as it requires the model to not only identify the object class,
but also the specific instance of that class. Object instance segmentation is used to identify
multiple instances of the same object class within an image or video, and the output of this
task is typically a mask or a binary image that indicates the presence of an object.
1.6 YOLOv7
YOLOv7 (You Only Look Once, version 7) is a state-of-the-art real-time object detection
algorithm that was developed by Alexey Bochkovskiy. It is a single-stage detector, which
means that it performs both object localization and classification in a single forward pass of
the convolutional neural network (CNN). This makes YOLOv7 particularly well-suited for
real-time object detection tasks where fast and accurate results are required. YOLOv7 uses a
new architecture that is more efficient than its predecessors, allowing it to run faster and with
less computational resources.
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It also incorporates several new features and improvements, including a new anchor scale
search algorithm, new data augmentation method, new depthwise separable convolution, and
a new architecture called SPP-Net (Spatial Pyramid Pooling Network) to improve the
detection of small objects. YOLOv7 has been shown to be highly accurate and efficient, with
a balance between speed and accuracy, and it is widely used in various real-world
applications, such as autonomous vehicles, surveillance, image retrieval, and object tracking.
1.7 Organization of the document
Chapter 1: Introduction
This chapter provides a general overview of the research work, including the purpose and
scope of the study. It will give a brief summary of the problem being addressed, the research
questions, and the main objectives of the study. Additionally, this chapter will give a general
idea of the content and structure of the upcoming chapters.
Chapter 2: Background and Literature Review
This chapter will provide an overview of the previous research in the field of object detection,
discussing the different methods that have been used and their applications. The chapter will
also provide a comprehensive review of the existing literature in the field, highlighting the
gaps in the current knowledge that the present study aims to address.
Chapter 3: Proposed System & Research Methodology
This chapter will provide a detailed description of the methods and techniques used to
conduct the research. It will explain the theoretical framework, research design, and data
collection and analysis procedures used in the study. The chapter will also present the results
of the research in a graphical and pictorial format, to provide a clear understanding of the
approach taken and the findings obtained.
Chapter 4: Overview of Software and Hardware Used
This chapter will describe the software and hardware used to implement the proposed system,
including the specific components and technologies used. It will provide an overview of the
tools and equipment used in the research and how they were employed to achieve the
research objectives.
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Chapter 5: Results and Discussion
This chapter will present the results of the proposed model for houses detection using object
detection. It will provide an in-depth analysis of the performance of the model, including
visual representations of the results, and a discussion of the findings. The chapter will also
evaluate the performance of the model using various metrics and provide insights into the
limitations of the proposed approach.
Chapter 6: Conclusion and Future Work
This chapter will summarize the main findings and conclusions of the research, highlighting
the key contributions of the proposed system for houses detection using object detection. It
will also discuss the limitations and challenges encountered during the research and propose
potential avenues for future work to improve the system. This chapter will give a brief
overview of the work discussed in the previous chapters.
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CHAPTER 2
LITERATURE REVIEW
2.1 Overview
Building detection in flooded areas is a challenging task in the field of object detection,
where the goal is to accurately identify buildings or houses using images. Floods are a
common natural disaster that can cause significant damage to property and infrastructure, and
as a result, obtaining a sufficient dataset for this task can be difficult. In this literature review
section, we will explore previous research in object detection that is closely related to this
topic and examine the current state of the art in building detection in flooded areas.
2.2 Previous Work
“Convolutional neural networks for object detection in aerial imagery for disaster response
and recovery”In this research, the authors employed a technique known as YOLO for
identifying objects within aerial images, with a focus on applications related to disaster
response and recovery. They trained and evaluated their models using an in-house dataset of
8 annotated aerial videos from various US hurricanes in 2017-2018. They achieved 80.69%
mAP for high altitude and 74.48% for low altitude footage. Furthermore, they also found that
models trained on similar altitude footage perform better and that using a balanced dataset
and pre-trained weights improves performance and reduces training time. YOLOv7 can
improve the performance and accuracy.[1]
“Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-
Resolution Images: A Case Study in Hunan Province, China” This paper presents a method
for identifying roof types of complex rural buildings using high-resolution UAV images,
which achieved F1-score, Kappa coefficient (KC) and Overall Accuracy (OA) averaging
0.777, 0.821 and 0.905 respectively. They use deep learning networks to analyze different
feature combinations .They found that the model incorporating Sobel edge detection features
had the highest accuracy.[2]
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“Flood Detection Based on Unmanned Aerial Vehicle System and Deep Learning” This
paper presents a study on the use of deep learning models for automated detection of flooded
buildings using UAV aerial images. The method was studied in a case study of Kangshan
embankment in Poyang Lake, and the results showed that flooding of central buildings and
vegetation could be recognized from images with 88% and 85% accuracy, respectively. The
research also shows that it is possible to estimate the buildings' inundation area according to
the UAV images and flight parameters. The study highlights the potential value of UAV
systems in providing accurate and timely visualization of the spatial distribution of
inundation for flood emergency response.[3]
“Drone-Based Water Level Detection in Flood Disasters” This paper presents research on
using aerial drone-based image recognition for fast and accurate assessment of flood damage.
In this work, we propose a water level detection system using an R-CNN learning model and
a new labeling method for reference objects such as houses and cars. This system uses data
augmentation and transfer-learning overlays of masked R-CNN for object detection models
to address the challenges of limited wild datasets of top-down flood images. Additionally, the
VGG16 network is employed for water level detection purposes. The system was evaluated
on realistic images captured at the time of a disaster and the results showed that the system
can achieve a detection accuracy of 73.42% with an error of 21.43 cm in estimating the water
level.[4]
“The application of UAV images in flood detection using image segmentation techniques”
This study presents research on the use of UAV-based image analysis for automated flood
detection. The study aims to develop a system that can automatically detect and analyze flood
severity using images captured by a UAV. The study utilizes RGB and HSI color models to
represent flood images and employs k-mean clustering and region growing for image
segmentation. The segmented images were validated with manually segmented images and
the results show that the region growing method using gray images has a better segmentation
accuracy of 88% compared to the k-mean clustering method. In this study, we also developed
an automatic flood monitoring system called Flood Detection Structure (FDS) based on the
domain extension method.[5]
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2.3 Research Summary
Various studies have been conducted using UAV and drone images for the detection of
flooded buildings, with the use of YOLO and Mask-RCNN being the most common
algorithms used. These studies have achieved high accuracy results, with some utilizing novel
techniques such as feature fusion and transfer learning. The result can be improved. However,
the process of manual annotation of the data remains a challenge in such research.
2.4 Scope of this problem
Using object detection includes developing an automated detection system that can accurately
identify flooded houses in images, using deep learning techniques and the YOLOv7 model.
The proposed method aims to provide efficient detection results by using YOLOv7.
Additionally, the scope of the problem also includes the need for automated annotation of
images. Which can make the system dynamic and save more time.
2.5 Challenges
The challenges include the limited availability of labeled datasets, difficulty in identifying
flooded houses in images due to similar appearance with other non-flooded structures, and
the need for accurate and efficient detection results. Additionally, the variability in lighting
and weather conditions, as well as the complexity of the flooded landscapes, can also pose
challenges in detecting flooded houses. The limited resources and time for collecting and
labeling the data is also one of the major challenges in this research.
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CHAPTER 3
METHODOLOGY
3.1 Working Procedure
This chapter provides an overview of the technical approach used to develop the system for
detecting flooded houses using object detection techniques. It explains the system
architecture, including the specific algorithms and techniques used for automated image
annotation and detection of buildings/houses in flooded areas. Additionally, it highlights any
challenges that were encountered during the development process and how they were
addressed.
3.2 Proposed System
Figure 1: Basic flowchart of the proposed system
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3.3 Data Collection
The collection and annotation of high-quality datasets is crucial for the development of any
real-world AI application, particularly in the field of object detection. However, obtaining
such datasets can be a challenging task due to the complexity and unstructured nature of real-
world data. This challenge is amplified in the case of detecting flooded houses, where the
availability of relevant and accurately labeled datasets is limited. In this research, we aimed
to address this challenge by collecting and annotating a dataset of 500 images from AIDER[6]
of flooded houses.We agumanted(Mosaic augmentation) the dataset and split it into 70%
training 20% validation and 10% test data. Here is the distribution:
● Train: (2048, 640, 640, 3)
● validation: (593, 640, 640, 3)
● Test: (283, 640, 640, 3)
Figure 2: Example dataset Images of Flooded houses
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3.4 Data Preprocessing
This chapter discusses the process of data pre-processing and cleaning, which is an essential
step in preparing the dataset for object detection. We began by removing any irrelevant or
noisy data from the dataset. This included removing images that did not belong to any of the
classes we were interested in. We also performed image resizing and brightness adjustments
to ensure a stable and consistent dataset for training and testing the object detection model.
3.4.1 Removing Unnecessary data
We preprocessed the dataset to ensure that it was clean and ready for training. This involved
removing any images that were noisy or did not represent flooded buildings. We also made
sure to remove any duplicate images, ensuring that the final dataset was as diverse and
representative as possible. Additionally, we also performed any necessary image adjustments,
such as brightness and contrast enhancements, to further improve the quality of the dataset.
This step was crucial in ensuring that our model was able to learn from the best possible data
and achieve high levels of accuracy during the training process.
3.4.2 Data Resizing
We resized all images to a standard resolution of 640×640 pixels with 3 channels to ensure
consistency across the dataset. This allows for better processing and training of our models.
Additionally, this ensures that all images are of the same size and aspect ratio, making it
easier to work with and analyze the data.
3.5 Image Annotation
The dataset must be labeled in order for the model to understand the relationship between the
input data and the desired output. However, in many cases, obtaining a labeled dataset can be
a time-consuming and labor-intensive process. In this study, we used an online annotation
tool called Roboflow to manually label our dataset for object detection. This involved
drawing bounding boxes around the objects of interest in the images and providing
appropriate labels for each object. Additionally, we also experimented with using different
methods of automatic annotation to label our images. This allowed us to quickly and
efficiently label our dataset, which is essential for training an accurate deep learning model.
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3.6 YOLOv7 Image Annotation
We used the yolov7 format for bounding box annotation, which is stored in .txt files. Each
row in the file represents one object and includes the class, x and y coordinates of the center,
and the width and height of the bounding box. These coordinates are normalized to the
dimensions of the image, with values between 0 and 1. It's important to note that class
numbers are zero-indexed, starting from 0.
Figure 3: YOLOv7 Annotation Format
Figure 4: YOLOv7 Annotated .txt File
Here in the Figure 4 the columns are (class, x_center, y_center, width, height) format.
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3.7 Automated Image Annotation
The process of image annotation is a crucial but time-consuming task in the field of computer
vision and object detection. It involves manually labeling images in a dataset with relevant
information, such as object class, location, and attributes. This is a labor-intensive task that
can take a significant amount of time and resources, especially when the dataset is large or
constantly changing. However, with the advancement of technology, it is now possible to
automate this process, making it more efficient and cost-effective. Automated image
annotation techniques, can be trained to recognize the object with high accuracy, reducing the
need for human intervention. This not only saves time and resources, but also ensures that the
dataset is up-to-date and accurate. By automating the image annotation process, it becomes
much more feasible to keep up with the rapid pace of data generation and improve the
performance of machine learning models. For our dataset, we used opencv library to achieve
our goal. The process is shown below:
Figure 5: Converting the image to Grayscale
To apply automated image annotation techniques, it is necessary to follow some essential
steps. One important step is to convert the images to grayscale and apply Gaussian blur to
remove small noise. This is an essential step that ensures the next process will have better
results.
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Figure 6: Apply Adaptive Canny Edge Detection
We used Adaptive Canny Edge Detection[7] is an improved version of the traditional Canny
Edge Detection algorithm that uses local image intensity to adjust the threshold values for the
hysteresis thresholding step, making it more robust to variations in the image intensity and
noise. By tuning the parameters, we can get the edges of buildings only.
For calculating the threshold values is to use the following equations:
Lower threshold = mean(image intensity) - k1 * std(image intensity)
Upper threshold = mean(image intensity) + k2 * std(image intensity)
Where k1 and k2 are constants that are used to control the threshold values, and mean and std
are the mean and standard deviation of the image intensity in the region around each pixel.
Figure 7: Apply Dilation & Erosion
We also applied Dilation to join the shape and thickening it, and Erosion to remove small
noises.
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Figure 8: Find Coordinates
After that, we can use the Contour Approximation Method to find the coordinate and the
bounding box from the binary image. We then converted the abounding box coordinate to
YOLO label format and got the annotated .txt file.
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CHAPTER 4
HARDWARE AND TOOLKIT
This chapter details the software and hardware components utilized in the development of our
system for detecting houses in flooded areas. The specific tools and technologies used to
implement the system will be discussed in depth.
4.1 Tools
The tools that will be utilized in the implementation of the proposed system include:
● Python
● NumPy
● Pandas
● OS
● Matplotlib
● OpenCV
● VS Code
● Colab Notebook
4.1.1 Python
Python is a widely-used, high-level programming language that is widely used in web
development, scientific computing, data analysis, artificial intelligence, and other fields. It
has a simple and easy-to-learn syntax, making it a popular choice for beginners and
experienced programmers alike. Python is also known for its large and active community,
which has developed a wide range of libraries and frameworks that make it easy to build
complex and powerful applications. These libraries provide a powerful and flexible set of
tools for building and training neural networks, which is the core technology behind our
system for detecting flooded houses. In this work, Python is used as a primary programming
language for implementing the deep learning model.
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4.1.2 NumPy
NumPy is a library in Python that is commonly used for image processing tasks. It provides a
powerful array object, as well as a number of functions for manipulating arrays, including
mathematical and statistical operations. One of the main advantages of using NumPy for
image processing is its ability to perform element-wise operations on arrays, which allows for
efficient implementation of many image processing algorithms. Additionally, NumPy can be
easily integrated with other libraries, such as OpenCV, making it a versatile tool for image
processing tasks.
It has a variety of attributes, such as the following:
● A powerful object for an N-dimensional array
● Advanced (broadcasting) features Tools for combining
● Fortran and C/C++ programs
● Useful Fourier transform, random number, and linear algebra abilities
● Can be used to store common data in a multidimensional format
● Can quickly and cleanly connect to a variety of databases
● Allows the generation of any data types
4.1.3 Pandas
Pandas is a Python library that is used for data manipulation and analysis. It provides
powerful data structures like the Data Frame and Series, which allows for easy manipulation
and analysis of large datasets. Pandas also has built-in functions for handling missing data,
merging and joining data, and filtering and grouping data. Additionally, it has strong support
for reading and writing data in various file formats such as CSV, Excel, and SQL. Pandas is a
crucial tool for data scientists and data analysts and is widely used in data wrangling, data
exploration, and data visualization tasks.
4.1.4 OS Module
The Python OS module provides tools for interacting with the operating system. It allows for
the use of operating system-dependent features and contains a variety of file system interface
functions through the os and os.path modules. These functions can be used for tasks such as
navigating file directories and manipulating files and directories.
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4.1.5 Matplotlib
Matplotlib is a powerful library for creating visualizations in Python. It can be used to create
static, animated, and interactive plots, making it a versatile tool for data exploration and
analysis. In our project, we utilized Matplotlib for visualizing the detected flooded houses by
plotting the images and displaying the results of our model. This made it easy to understand
the performance of our model and gain insights from the data. Overall, Matplotlib played an
important role in our project by providing a clear and intuitive way to present our findings.
4.1.6 OpenCV
In this project, we used OpenCV, which is an open-source computer vision library, to
perform various image processing tasks. This library contains a wide range of tools and
functions that can be used to process and analyze images and videos. In our project, we used
OpenCV to read, display, and manipulate images. We also used it to perform image cropping,
resizing, and thresholding to enhance the image quality. Additionally, OpenCV's feature
detection and extraction capabilities were used to identify objects in the flooded images,
which is an important step in object detection. Overall, OpenCV proved to be a valuable tool
in this project as it allowed us to perform various image processing tasks efficiently and
effectively.
4.1.7 VS Code
Visual Studio Code (VS Code) is a popular source-code editor that is widely used by
developers for its powerful features and ease of use. Developed by Microsoft, it is available
for Windows, Linux, and macOS and is designed for building and debugging modern web
and cloud applications. Some of the key features of VS Code include debugging, syntax
highlighting, intelligent code completion, and code refactoring, as well as support for a wide
range of programming languages and a large number of customizable extensions. It is
considered as a lightweight, fast and flexible code editor.
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4.1.8 Colab Notebook
Colab Notebook is a web-based platform for machine learning development. It is a free,
open-source Jupyter notebook environment that requires no setup and runs entirely in the
cloud. With Colab Notebook, you can write, execute, and share code with others, as well as
import data, train models, and collaborate with others in real-time. It also allows you to use
powerful hardware such as GPUs and TPUs for training models. It also provides a seamless
integration with Google Drive, which makes it easy to store and access your data and models.
Overall, Colab Notebook is a great tool for data scientists, machine learning engineers, and
researchers, who need a powerful and easy-to-use environment for their work.
4.2 Hardware
● Processor : Intel(R) Core(TM) i3-8130U CPU @ 2.20GHz 2.21 GHz
● Ram : 8 GB DDR4, 2400MHz
● OS : Windows 10
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CHAPTER 5
RESULT & DISCUSSION
This chapter presents the results and performance analysis of our proposed system, YOLOv7,
on our dataset of flood affected building images. The metrics used include accuracy,
precision, recall, and mAP. Additionally, the chapter includes visual representations of the
comparison between actual and predicted results for both the automatic and manual
annotation methods.
5.1 Performance Evaluation
The mAP (mean Average Precision) metric is commonly used to evaluate the performance of
object detection models, such as YOLO. It is calculated by taking into account various factors
including the Intersection over Union (IOU), precision, recall, and the precision-recall curve.
The mAP score provides a comprehensive measure of the model's overall accuracy.
5.1.1 IOU
IOU is a metric used to evaluate the performance of object detection models such as YOLO,
by measuring the overlap between predicted and ground truth bounding boxes. mAP is the
mean Average Precision, which takes into account the IOU threshold for successful detection.
Figure 9: IOU
mAP50 is the accuracy when IOU is set at 50%, meaning that if there is more than 50%
overlap between the predicted and ground truth bounding boxes, it is considered a correct
detection. The higher the IOU threshold, the stricter the evaluation, and thus the lower the
mAP value.
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5.1.2 Precision and Recall
Precision is a metric that measures the accuracy of positive predictions. It is the ratio of true
positive detections to the total number of detections, including false positives. A precision of
1.0 means all positive predictions were correct, while a lower value indicates false positives.
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 =
𝑃𝑃
𝑃𝑃 + 𝑃𝑃
Precision measures the proportion of correctly identified positive instances, while recall
measures the proportion of actual positive instances that were correctly identified. A model
with high precision and high recall is considered to be accurate.
𝑃𝑃𝑃𝑃𝑃𝑃 =
𝑃𝑃
𝑃𝑃 + 𝑃𝑃
5.1.3 Average Precision
Average Precision (AP) is a commonly used metric in object detection to evaluate the
performance of a model. It is calculated by measuring the area under the Precision-Recall
Curve. AP is considered a more comprehensive metric as it takes into account both precision
and recall, providing a more accurate assessment of the model's performance. A higher AP
value indicates that the model is able to identify more relevant objects and minimize false
positives, resulting in a better-performing model.
5.1.4 Mean Average Precision
The metric of mAP (mean Average Precision) is a useful tool for evaluating the performance
of object detection models. It takes into account both precision and recall by averaging the
AP (Average Precision) values for all classes in the model. A higher mAP value indicates a
more accurate and efficient model. It is commonly used to compare the performance of
different models and to identify areas for improvement.
𝑃𝑃𝑃 =
1
𝑃
∑𝑃
𝑃=1 𝑃𝑃𝑃
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5.2 Experimental Analysis
The experiments have been separated into two parts. In the first part, we evaluate the
performance of our proposed system, YOLOv7, applied to our manually annotated dataset.
The dataset consists of two classes, buildings and vegetation, which were labeled by human
experts. We trained the YOLOv7 model using this dataset and calculated the accuracy,
precision, recall, and mAP. In the second part of the experiments, we applied the YOLOv7
model on the automated annotated dataset and compare the results with the manually
annotated dataset. Overall, our experiments aim to provide a comprehensive evaluation of the
performance of YOLOv7 on our flood affected building detection dataset.
5.3 YOLOv7 On Manually Annotated Dataset
We trained the Manually Annotated Dataset on YOLOv7, and the results were quite
impressive. The model was able to achieve a high level of accuracy, with an overall mAP of
around 0.92. The precision and recall were both very high, with precision being around 0.90
and recall being around 0.86. This indicates that the model was able to accurately detect and
classify the buildings and vegetation in the images with a high degree of accuracy. Overall,
the results of this experiment were very promising and demonstrate the potential of YOLOv7
for use in flood detection systems.
5.3.1 Object Detection Report
Figure 10: Object Detection Report For Manually Annotated Data
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5.3.2 Confusion Matrix
Figure 11: Confusion Matrix For Manually Annotated Data
5.3.3 Accuracy & Loss Curve
Figure 11: Accuracy & Loss Curve For Manually Annotated Data
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5.4 YOLOv7 On Automatic Annotated Dataset
We trained the YOLOv7 model on the Automated Annotated Dataset, and it performed little
less than the previous model. The model achieved a high level of accuracy, with an overall
mAP of around 0.73. It had a good precision of around 0.79, and recall of around 0.69. The
use of automated annotation system not only saves time but also gives the promising results.
The results of this experiment indicate that YOLOv7 could be an effective tool for detecting
flooded buildings in images.
5.4.1 Object Detection Report
Figure 10: Object Detection Report For Automatic Annotated Data
5.4.2 Confusion Matrix
Figure 11: Confusion Matrix For Automatic Annotated Data
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5.4.3 Accuracy & Loss Curve
Figure 11: Accuracy & Loss Curve For Automatic Annotated Data
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5.5 Comparison of Performance
YOLOv7
(manual
annotation)
YOLOv7
(Automatic
annotation)
Precision
0.90 0.79
Recall
0.86 0.69
mAP@0.5
Accuracy 0.92 0.73
Table 1: Model Comparison
The results from both the manually annotated dataset and the automated annotated dataset
were compared and analyzed. The model trained on the manually annotated dataset showed a
slightly higher mAP of around 0.78 compared to the model trained on the automated
annotated dataset, which had a mAP of around 0.73. Both models had similar precision and
recall scores, with the manually annotated model having slightly higher precision at 0.83 and
the automated annotated model having slightly higher recall at 0.71. However, the use of an
automated annotation system greatly reduced the time and resources needed for annotation,
highlighting the potential benefits of this approach. Overall, both models showed promising
results in detecting flooded buildings in images.
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CHAPTER 6
CONCLUSION & FUTURE WORK
This chapter presents the conclusions and evaluations of our proposed system, based on the
results and observations from the experiments. It also highlights the limitations of the current
research and suggests potential future work to improve the system's performance.
6.1 Conclusion
We compared the results of training YOLOv7 on both manual and automated annotated
datasets and found that the model achieved a higher accuracy with the manual annotated
dataset, with an overall mAP of around 92%. However, using an automated annotation
system saved a significant amount of time. Overall, the results of this comparison
demonstrate the potential for using YOLOv7 in flood detection systems and the benefits of
using an automated annotation system to speed up the process.
6.2 Limitation
The study used a few images for training and testing the model, which may not be
representative of all possible flood scenarios. It only focused on detecting flooded buildings,
and did not consider other types of flooding, such as road flooding or flash flooding.
6.3 Future Work
We will continue analyzing and try to improve the model and try to build projects with it.
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CHAPTER 7
REFERENCES
7.1 References
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