This document reviews improving traffic sign detection using the YOLO algorithm for object detection. It begins by discussing previous work on traffic sign detection and recognition that used techniques like mobile LiDAR, sparse R-CNN neural networks, and improvements to YOLOv4-Tiny. It then examines the YOLO algorithm and how it uses convolutional neural networks for real-time object detection with a single propagation through the network. The document proposes using an improved YOLO algorithm for traffic sign detection to address limitations in existing techniques. It discusses the methodology of object detection, recognition and localization using neural networks and how YOLO has been applied for applications like traffic sign detection.
Traffic Sign Detection and Recognition for Automated Driverless Cars Based on...ijtsrd
Self driving vehicles are cars or trucks in which human drivers are never required to take control to safely operate the vehicle. They can possibly reform urban portability by giving maintainable, protected, and advantageous, clog free transportability. The issues like reliably perceiving traffic lights, signs, indistinct path markings can be overwhelmed by utilizing the innovative improvement in the fields of Deep Learning DL . Here, Faster Region Based Convolution Neural Network F RCNN is proposed for detection and recognition of Traffic Lights TL and signs by utilizing transfer learning. The input can be taken from the dataset containing various images of traffic signals and signs as per Indian Traffic Signals. The model achieves its target by distinguishing the traffic light and signs with its right class type. The proposed framework can likewise be upgraded for safe driving in spite of hazy path markings. Aswathy Madhu | Veena S Nair "Traffic Sign Detection and Recognition for Automated Driverless Cars Based on SSD" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31888.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31888/traffic-sign-detection-and-recognition-for-automated-driverless-cars-based-on-ssd/aswathy-madhu
An Analysis of Various Deep Learning Algorithms for Image Processingvivatechijri
Various applications of image processing has given it a wider scope when it comes to data analysis.
Various Machine Learning Algorithms provide a powerful environment for training modules effectively to
identify various entities of images and segment the same accordingly. Rather one can observe that though the
image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task,
deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known
and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the
image processing domain. It has way higher accuracy and computational power for classifying images further
and segregating their various entities as individual components of the image working region. Major focus will
be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level
segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.
Traffic Sign Detection and Recognition for Automated Driverless Cars Based on...ijtsrd
Self driving vehicles are cars or trucks in which human drivers are never required to take control to safely operate the vehicle. They can possibly reform urban portability by giving maintainable, protected, and advantageous, clog free transportability. The issues like reliably perceiving traffic lights, signs, indistinct path markings can be overwhelmed by utilizing the innovative improvement in the fields of Deep Learning DL . Here, Faster Region Based Convolution Neural Network F RCNN is proposed for detection and recognition of Traffic Lights TL and signs by utilizing transfer learning. The input can be taken from the dataset containing various images of traffic signals and signs as per Indian Traffic Signals. The model achieves its target by distinguishing the traffic light and signs with its right class type. The proposed framework can likewise be upgraded for safe driving in spite of hazy path markings. Aswathy Madhu | Veena S Nair "Traffic Sign Detection and Recognition for Automated Driverless Cars Based on SSD" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31888.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31888/traffic-sign-detection-and-recognition-for-automated-driverless-cars-based-on-ssd/aswathy-madhu
An Analysis of Various Deep Learning Algorithms for Image Processingvivatechijri
Various applications of image processing has given it a wider scope when it comes to data analysis.
Various Machine Learning Algorithms provide a powerful environment for training modules effectively to
identify various entities of images and segment the same accordingly. Rather one can observe that though the
image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task,
deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known
and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the
image processing domain. It has way higher accuracy and computational power for classifying images further
and segregating their various entities as individual components of the image working region. Major focus will
be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level
segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.
A hierarchical RCNN for vehicle and vehicle license plate detection and recog...IJECEIAES
Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.
Traffic Accident Detection
The Problem is to build a reliable and accurate traffic accident detection system using state-of-the-art object detection models. The system should be able to analyze live video feeds from CCTV cameras and determine whether an accident has occurred or not, based on the presence of specific visual cues such as collision, and vehicle damage. The system's performance will be evaluated based on accuracy, precision, recall, and computational efficiency.
Faster R-CNN (ResNet-50)
YOLO_V5_ Overview
SSD_mobilenet_v2_320*320
A hierarchical RCNN for vehicle and vehicle license plate detection and recog...IJECEIAES
Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.
Traffic Accident Detection
The Problem is to build a reliable and accurate traffic accident detection system using state-of-the-art object detection models. The system should be able to analyze live video feeds from CCTV cameras and determine whether an accident has occurred or not, based on the presence of specific visual cues such as collision, and vehicle damage. The system's performance will be evaluated based on accuracy, precision, recall, and computational efficiency.
Faster R-CNN (ResNet-50)
YOLO_V5_ Overview
SSD_mobilenet_v2_320*320
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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.
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.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
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.
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
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