This document presents a traffic sign detection, recognition, and notification system using Faster R-CNN. The system takes video input containing traffic signs and converts it to frames. Faster R-CNN with ROI pooling and a classifier is used to detect traffic signs. Color and shape information are then used to refine detections. A CNN classifier recognizes the signs. The system notifies drivers of detected signs via audio messages, helping drivers comply with signs even if ignored visually. The proposed detector detects all sign categories, and recognition accuracy on the German Traffic Sign Detection Benchmark dataset exceeds 90% for 42 sign classes.
Automatic License Plate Recognition Using Optical Character Recognition Based...IJARIIE JOURNAL
A License plate is a rectangular plate which is alphanumeric. The license plate is fixed on the vehicle and used to
identify the vehicle along with honor of that vehicle. There is a huge number of vehicles on the road so that traffic
control and vehicle owner identification has become a major problem.
The automatic number plate reorganization (ANPR) is one of the solutions of such kind of problem. There are
different methodologies but it is challenging task as some of the factors like high speed of vehicles, languages of
number plate & mostly non-uniform letter on number plate effects a lot in recognition. The license plate recognition
system mainly has four stages: image acquisition, license plate detection, character segmentation and character
recognition. The license plate recognition (LPR) system have many applications like payment of parking fees; toll
fee on the highway; traffic monitoring system; border security system; signal system etc.
In this paper, template matching algorithm for character recognition is used. The system presented here mainly
focuses on recognition of ambiguous characters based on position of the character. It is observed that the developed
system successfully detects & recognizes the vehicle number plate on real images and the problem of recognizing
ambiguous character is solved.
Automated License Plate detection and Speed estimation of Vehicle Using Machi...ijtsrd
A well ordered traffic management system is required in all types of roads, such as off roads, highways, etc. There has been several laws and speed controlled measures are taken in all places with different perspectives. Also Speed limit may vary from road to road. So there are number of methods has been proposed using computer Vision and machine learning algorithms for object tracking. Here vehicles are recognized and detected from the videos that taken using surveillance camera. The aim is to identification of the vehicles and tracking using Haar Classifier, then determine the speed of the vehicle and Finally Detecting the License plate of the vehicle. Detecting the License plate and vehicle speed using machine learning is tough but beneficial task. For the past few years Convolution Neural Network CNN has been widely used in computer vision for vehicle detection and identification. Dlibs are used to track the multiple objects at the same time. P. Devi Mahalakshmi | Dr. M. Babu "Automated License Plate detection and Speed estimation of Vehicle Using Machine Learning - Haar Classifier Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33395.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/33395/automated-license-plate-detection-and-speed-estimation-of-vehicle-using-machine-learning--haar-classifier-algorithm/p-devi-mahalakshmi
Automatic License Plate Recognition Using Optical Character Recognition Based...IJARIIE JOURNAL
A License plate is a rectangular plate which is alphanumeric. The license plate is fixed on the vehicle and used to
identify the vehicle along with honor of that vehicle. There is a huge number of vehicles on the road so that traffic
control and vehicle owner identification has become a major problem.
The automatic number plate reorganization (ANPR) is one of the solutions of such kind of problem. There are
different methodologies but it is challenging task as some of the factors like high speed of vehicles, languages of
number plate & mostly non-uniform letter on number plate effects a lot in recognition. The license plate recognition
system mainly has four stages: image acquisition, license plate detection, character segmentation and character
recognition. The license plate recognition (LPR) system have many applications like payment of parking fees; toll
fee on the highway; traffic monitoring system; border security system; signal system etc.
In this paper, template matching algorithm for character recognition is used. The system presented here mainly
focuses on recognition of ambiguous characters based on position of the character. It is observed that the developed
system successfully detects & recognizes the vehicle number plate on real images and the problem of recognizing
ambiguous character is solved.
Automated License Plate detection and Speed estimation of Vehicle Using Machi...ijtsrd
A well ordered traffic management system is required in all types of roads, such as off roads, highways, etc. There has been several laws and speed controlled measures are taken in all places with different perspectives. Also Speed limit may vary from road to road. So there are number of methods has been proposed using computer Vision and machine learning algorithms for object tracking. Here vehicles are recognized and detected from the videos that taken using surveillance camera. The aim is to identification of the vehicles and tracking using Haar Classifier, then determine the speed of the vehicle and Finally Detecting the License plate of the vehicle. Detecting the License plate and vehicle speed using machine learning is tough but beneficial task. For the past few years Convolution Neural Network CNN has been widely used in computer vision for vehicle detection and identification. Dlibs are used to track the multiple objects at the same time. P. Devi Mahalakshmi | Dr. M. Babu "Automated License Plate detection and Speed estimation of Vehicle Using Machine Learning - Haar Classifier Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33395.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/33395/automated-license-plate-detection-and-speed-estimation-of-vehicle-using-machine-learning--haar-classifier-algorithm/p-devi-mahalakshmi
CANNY EDGE DETECTION BASED REAL-TIME INTELLIGENT PARKING MANAGEMENT SYSTEMJANAK TRIVEDI
Real-time traffic monitoring and parking are very important aspects
for a better social and economic system. Python-based Intelligent Parking
Management System (IPMS) module using a USB camera and a canny edge
detection method was developed. The current situation of real-time parking slot
was simultaneously checked, both online and via a mobile application, with a
message of Parking “Available” or “Not available” for 10 parking slots. In
addition, at the time entering in parking module, gate open and at the time of exit
parking module, the gate closes automatically using servomotor and sensors.
Results are displayed in figures with the proposed method flow chart
Classification and Detection of Vehicles using Deep Learningijtsrd
The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. The traditional methods of vehicle classification and detection are highly complex which provides coarse grained results due to suffering from limited viewpoints. Because of the latest achievements of Deep Learning, it was successfully applied to image classification and detection of objects. This paper presents a method based on a convolutional neural network, which consists of two steps vehicle classification and vehicle license plate recognition. Several typical neural network modules have been applied in training and testing the vehicle Classification and detection of license plate model, such as CNN convolutional neural networks , TensorFlow, Tesseract OCR. The proposed method can identify the vehicle type, number plate and other information accurately. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original training dataset and enriched testing dataset, the algorithm can obtain results with an average accuracy of about 97.32 in the classification and detection of vehicles. By increasing the amount of the data, the mean error and misclassification rate gradually decreases. So, this algorithm which is based on Deep Learning has good superiority and adaptability. When compared to the leading methods in the challenging Image datasets, our deep learning approach obtains highly competitive results. Finally, this paper proposes modern methods for the improvement of the algorithm and prospects the development direction of deep learning in the field of machine learning and artificial intelligence. Madde Pavan Kumar | Dr. K. Manivel | N. Jayanthi "Classification & Detection of Vehicles using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30353.pdf Paper Url :https://www.ijtsrd.com/engineering/software-engineering/30353/classification-and-detection-of-vehicles-using-deep-learning/madde-pavan-kumar
: This paper is aimed at designing a density based dynamic traffic signal system where the timing
of signal will change automatically on sensing the traffic density at any junction using the IoT technology. Traffic
congestion is a severe problem in most cities across the world and therefore it is time to shift more manual mode
or fixed timer mode to an automated system with decision making capabilities. To optimize this problem, we have
made a framework for an intelligent traffic control system. Sometimes higher traffic density at one side of the
junction demands longer green time as compared to standard allotted time. We therefore propose here a
mechanism in which the time period of green light and red light is assigned on the basis of the density of the
traffic present at the time. This is achieved by using LIDAR sensors.
A VISION-BASED REAL-TIME ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM USING VEHICULA...JANAK TRIVEDI
In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.
Number Plate Recognition for Indian Vehiclesmonjuri10
This paper presents Automatic Number Plate
extraction, character segmentation and recognition for
Indian vehicles. In India, number plate models are not
followed strictly. Characters on plate are in different
Indian languages, as well as in English. Due to variations
in the representation of number plates, vehicle number
plate extraction, character segmentation and recognition
are crucial. We present the number plate extraction,
character segmentation and recognition work, with english
characters. Number plate extraction is done using Sobel
filter, morphological operations and connected component
analysis. Character segmentation is done by using
connected component and vertical projection analysis.
Character recognition is carried out using Support Vector
machine (SVM). The segmentation accuracy is 80% and
recognition rate is 79.84 %.
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACHJANAK TRIVEDI
We present vehicle detection classification using the Convolution
Neural Network (CNN) of the deep learning approach. The automatic vehicle
classification for traffic surveillance video systems is challenging for the Intelligent
Transportation System (ITS) to build a smart city. In this article, three different
vehicles: bike, car and truck classification are considered for around 3,000 bikes,
6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract
different vehicle dataset’s different features without a manual selection of features.
The accuracy of CNN is measured in terms of the confidence values of the detected
object. The highest confidence value is about 0.99 in the case of the bike category
vehicle classification. The automatic vehicle classification supports building an
electronic toll collection system and identifying emergency vehicles in the traffic
A Novel Multiple License Plate Extraction Technique for Complex Background in...CSCJournals
License plate recognition (LPR) is one of the most important applications of applying computer techniques towards intelligent transportation systems (ITS). In order to recognize a license plate efficiently, location and extraction of the license plate is the key step. Hence finding the position of a license plate in a vehicle image is considered to be the most crucial step of an LPR system, and this in turn greatly affects the recognition rate and overall speed of the whole system. This paper mainly deals with the detecting license plate location issues in Indian traffic conditions. The vehicles in India sometimes bare extra textual regions such as owner’s name, symbols, popular sayings and advertisement boards in addition to license plate. Situation insists for accurate discrimination of text class and fine aspect ratio analysis. In addition to this additional care taken up in this paper is to extract license plate of motorcycle (size of plate is small and double row plate), car (single as well as double row type), transport system such as bus, truck, (dirty plates) as well as multiple license plates present in an image frame under consideration. Disparity of aspect ratios is a typical feature of Indian traffic. Proposed method aims at identifying region of interest by performing a sequence of directional segmentation and morphological processing. Always the first step is of contrast enhancement, which is accomplished by using sigmoid function. In the subsequent steps, connected component analysis followed by different filtering techniques like aspect ratio analysis and plate compatible filter technique is used to find exact license plate. The proposed method is tested on large database consisting of 750 images taken in different conditions. The algorithm could detect the license plate in 742 images with success rate of 99.2%.
Vehicle plate recognition is a successful image processing technique used to recognize vehicles' plate numbers. There are several applications for this method which enlarge through many fields and attention groups. Vehicle plate recognition may be considered as an advertising equipment, for the purpose of traffic and border securities for law enforcement, and travel. Many methods have been accompanied to make this technique easy. This learning proposes an edge-detection method to allow a Plate Recognition System of a vehicle through the practical situations like the various environmental or meteorological conditions. Image processing tools are used to examine the plate area, resize it, and change it on the way to a gray scale earlier to filtering of the image in order to remove the unwanted areas. The obtained objects is processed in such a way that the number plate image and the information related to that is completely perfect The information of the obtained image is processed through the average deviation of the Gaussian filter (sigma).
OpenCVand Matlab based Car Parking System Module for Smart City using Circle ...JANAK TRIVEDI
finding parking availability for a specific time period is
a very tedious job in urban areas. The Indian government now
focusing on t he smart city project, already they published city
name for a n upcoming smart city project. In smart city
application , intelligent transportation system (ITS) plays an
important role- in that finding parking place, specifically for the
car owner to avoid time computation, as well as congestion in
traffic is going to be very important. In this article, we propose
an intelligent car parking system for the smart city using Circle
Hough Transform (CHT).
Traffic Light Detection and Recognition for Self Driving Cars using Deep Lear...ijtsrd
Self driving cars has the potential to revolutionize urban mobility by providing sustainable, safe, and convenient and congestion free transportability. Autonomous driving vehicles have become a trend in the vehicle industry. Many driver assistance systems DAS have been presented to support these automatic cars. This vehicle autonomy as an application of AI has several challenges like infallibly recognizing traffic lights, signs, unclear lane markings, pedestrians, etc. These problems can be overcome by using the technological development in the fields of Deep Learning, Computer Vision due to availability of Graphical Processing Units GPU and cloud platform. By using deep learning, a deep neural network based model is proposed for reliable detection and recognition of traffic lights TL . Aswathy Madhu | Sruthy S ""Traffic Light Detection and Recognition for Self Driving Cars using Deep Learning: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30030.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30030/traffic-light-detection-and-recognition-for-self-driving-cars-using-deep-learning-survey/aswathy-madhu
A Review: Machine vision and its ApplicationsIOSR Journals
Abstract:The machine vision has been used in the industrial machine designing by using the intelligent character recognition. Due to its increased use, it makes the significant contribution to ensure the competitiveness in modern development. The state of art in machine vision inspection and a critical overview of applications in various industries are presented in this paper. In its restricted sense it is also known as the computer vision or the robot vision. This paper gives the overview of Machine Vision Technology in the first section, followed by various industrial application and thefuture trends in Machine Vision. Keywords:CCD- charged coupled devices, Fruit harvesting system, HIS- Hue Saturation Intensity, Image analysis, Image enhancement, Image feature extraction, Image feature classification processing, Intelligent Vehicle tracking , Isodiscriminationn Contour, Machine Vision
Projection Profile Based Number Plate Localization and Recognition csandit
This paper proposes algorithms to localize vehicle
number plates from natural background
images, to segment the characters from the localize
d number plates and to recognize the
segmented characters. The reported system is tested
on a dataset of 560 sample images
captured with different background under various il
luminations. The performance accuracy of
the proposed system has been calculated at each sta
ge, which is 97.1%, 95.4% and 95.72% for
localisation & extraction, character segmentation a
nd character recognition respectively. The
proposed method is also capable of localising and r
ecognising multiple number plates in
images.
CANNY EDGE DETECTION BASED REAL-TIME INTELLIGENT PARKING MANAGEMENT SYSTEMJANAK TRIVEDI
Real-time traffic monitoring and parking are very important aspects
for a better social and economic system. Python-based Intelligent Parking
Management System (IPMS) module using a USB camera and a canny edge
detection method was developed. The current situation of real-time parking slot
was simultaneously checked, both online and via a mobile application, with a
message of Parking “Available” or “Not available” for 10 parking slots. In
addition, at the time entering in parking module, gate open and at the time of exit
parking module, the gate closes automatically using servomotor and sensors.
Results are displayed in figures with the proposed method flow chart
Classification and Detection of Vehicles using Deep Learningijtsrd
The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. The traditional methods of vehicle classification and detection are highly complex which provides coarse grained results due to suffering from limited viewpoints. Because of the latest achievements of Deep Learning, it was successfully applied to image classification and detection of objects. This paper presents a method based on a convolutional neural network, which consists of two steps vehicle classification and vehicle license plate recognition. Several typical neural network modules have been applied in training and testing the vehicle Classification and detection of license plate model, such as CNN convolutional neural networks , TensorFlow, Tesseract OCR. The proposed method can identify the vehicle type, number plate and other information accurately. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original training dataset and enriched testing dataset, the algorithm can obtain results with an average accuracy of about 97.32 in the classification and detection of vehicles. By increasing the amount of the data, the mean error and misclassification rate gradually decreases. So, this algorithm which is based on Deep Learning has good superiority and adaptability. When compared to the leading methods in the challenging Image datasets, our deep learning approach obtains highly competitive results. Finally, this paper proposes modern methods for the improvement of the algorithm and prospects the development direction of deep learning in the field of machine learning and artificial intelligence. Madde Pavan Kumar | Dr. K. Manivel | N. Jayanthi "Classification & Detection of Vehicles using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30353.pdf Paper Url :https://www.ijtsrd.com/engineering/software-engineering/30353/classification-and-detection-of-vehicles-using-deep-learning/madde-pavan-kumar
: This paper is aimed at designing a density based dynamic traffic signal system where the timing
of signal will change automatically on sensing the traffic density at any junction using the IoT technology. Traffic
congestion is a severe problem in most cities across the world and therefore it is time to shift more manual mode
or fixed timer mode to an automated system with decision making capabilities. To optimize this problem, we have
made a framework for an intelligent traffic control system. Sometimes higher traffic density at one side of the
junction demands longer green time as compared to standard allotted time. We therefore propose here a
mechanism in which the time period of green light and red light is assigned on the basis of the density of the
traffic present at the time. This is achieved by using LIDAR sensors.
A VISION-BASED REAL-TIME ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM USING VEHICULA...JANAK TRIVEDI
In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.
Number Plate Recognition for Indian Vehiclesmonjuri10
This paper presents Automatic Number Plate
extraction, character segmentation and recognition for
Indian vehicles. In India, number plate models are not
followed strictly. Characters on plate are in different
Indian languages, as well as in English. Due to variations
in the representation of number plates, vehicle number
plate extraction, character segmentation and recognition
are crucial. We present the number plate extraction,
character segmentation and recognition work, with english
characters. Number plate extraction is done using Sobel
filter, morphological operations and connected component
analysis. Character segmentation is done by using
connected component and vertical projection analysis.
Character recognition is carried out using Support Vector
machine (SVM). The segmentation accuracy is 80% and
recognition rate is 79.84 %.
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACHJANAK TRIVEDI
We present vehicle detection classification using the Convolution
Neural Network (CNN) of the deep learning approach. The automatic vehicle
classification for traffic surveillance video systems is challenging for the Intelligent
Transportation System (ITS) to build a smart city. In this article, three different
vehicles: bike, car and truck classification are considered for around 3,000 bikes,
6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract
different vehicle dataset’s different features without a manual selection of features.
The accuracy of CNN is measured in terms of the confidence values of the detected
object. The highest confidence value is about 0.99 in the case of the bike category
vehicle classification. The automatic vehicle classification supports building an
electronic toll collection system and identifying emergency vehicles in the traffic
A Novel Multiple License Plate Extraction Technique for Complex Background in...CSCJournals
License plate recognition (LPR) is one of the most important applications of applying computer techniques towards intelligent transportation systems (ITS). In order to recognize a license plate efficiently, location and extraction of the license plate is the key step. Hence finding the position of a license plate in a vehicle image is considered to be the most crucial step of an LPR system, and this in turn greatly affects the recognition rate and overall speed of the whole system. This paper mainly deals with the detecting license plate location issues in Indian traffic conditions. The vehicles in India sometimes bare extra textual regions such as owner’s name, symbols, popular sayings and advertisement boards in addition to license plate. Situation insists for accurate discrimination of text class and fine aspect ratio analysis. In addition to this additional care taken up in this paper is to extract license plate of motorcycle (size of plate is small and double row plate), car (single as well as double row type), transport system such as bus, truck, (dirty plates) as well as multiple license plates present in an image frame under consideration. Disparity of aspect ratios is a typical feature of Indian traffic. Proposed method aims at identifying region of interest by performing a sequence of directional segmentation and morphological processing. Always the first step is of contrast enhancement, which is accomplished by using sigmoid function. In the subsequent steps, connected component analysis followed by different filtering techniques like aspect ratio analysis and plate compatible filter technique is used to find exact license plate. The proposed method is tested on large database consisting of 750 images taken in different conditions. The algorithm could detect the license plate in 742 images with success rate of 99.2%.
Vehicle plate recognition is a successful image processing technique used to recognize vehicles' plate numbers. There are several applications for this method which enlarge through many fields and attention groups. Vehicle plate recognition may be considered as an advertising equipment, for the purpose of traffic and border securities for law enforcement, and travel. Many methods have been accompanied to make this technique easy. This learning proposes an edge-detection method to allow a Plate Recognition System of a vehicle through the practical situations like the various environmental or meteorological conditions. Image processing tools are used to examine the plate area, resize it, and change it on the way to a gray scale earlier to filtering of the image in order to remove the unwanted areas. The obtained objects is processed in such a way that the number plate image and the information related to that is completely perfect The information of the obtained image is processed through the average deviation of the Gaussian filter (sigma).
OpenCVand Matlab based Car Parking System Module for Smart City using Circle ...JANAK TRIVEDI
finding parking availability for a specific time period is
a very tedious job in urban areas. The Indian government now
focusing on t he smart city project, already they published city
name for a n upcoming smart city project. In smart city
application , intelligent transportation system (ITS) plays an
important role- in that finding parking place, specifically for the
car owner to avoid time computation, as well as congestion in
traffic is going to be very important. In this article, we propose
an intelligent car parking system for the smart city using Circle
Hough Transform (CHT).
Traffic Light Detection and Recognition for Self Driving Cars using Deep Lear...ijtsrd
Self driving cars has the potential to revolutionize urban mobility by providing sustainable, safe, and convenient and congestion free transportability. Autonomous driving vehicles have become a trend in the vehicle industry. Many driver assistance systems DAS have been presented to support these automatic cars. This vehicle autonomy as an application of AI has several challenges like infallibly recognizing traffic lights, signs, unclear lane markings, pedestrians, etc. These problems can be overcome by using the technological development in the fields of Deep Learning, Computer Vision due to availability of Graphical Processing Units GPU and cloud platform. By using deep learning, a deep neural network based model is proposed for reliable detection and recognition of traffic lights TL . Aswathy Madhu | Sruthy S ""Traffic Light Detection and Recognition for Self Driving Cars using Deep Learning: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30030.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30030/traffic-light-detection-and-recognition-for-self-driving-cars-using-deep-learning-survey/aswathy-madhu
A Review: Machine vision and its ApplicationsIOSR Journals
Abstract:The machine vision has been used in the industrial machine designing by using the intelligent character recognition. Due to its increased use, it makes the significant contribution to ensure the competitiveness in modern development. The state of art in machine vision inspection and a critical overview of applications in various industries are presented in this paper. In its restricted sense it is also known as the computer vision or the robot vision. This paper gives the overview of Machine Vision Technology in the first section, followed by various industrial application and thefuture trends in Machine Vision. Keywords:CCD- charged coupled devices, Fruit harvesting system, HIS- Hue Saturation Intensity, Image analysis, Image enhancement, Image feature extraction, Image feature classification processing, Intelligent Vehicle tracking , Isodiscriminationn Contour, Machine Vision
Projection Profile Based Number Plate Localization and Recognition csandit
This paper proposes algorithms to localize vehicle
number plates from natural background
images, to segment the characters from the localize
d number plates and to recognize the
segmented characters. The reported system is tested
on a dataset of 560 sample images
captured with different background under various il
luminations. The performance accuracy of
the proposed system has been calculated at each sta
ge, which is 97.1%, 95.4% and 95.72% for
localisation & extraction, character segmentation a
nd character recognition respectively. The
proposed method is also capable of localising and r
ecognising multiple number plates in
images.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.