Intelligent Traffic Monitoring: YOLO v8 and CSV Data Integration - Suchi Pandey; Abhishek Yadav; Sachin Nirmal; Sarvesh Kumar (International Journal of Electronics Automation, 2024)
The “Intelligent Traffic Monitoring: YOLO v8 and CSV Data Integration” project is a cutting-edge solution for intelligent traffic monitoring, with YOLO v8 (You Only Look Once) serving as the fundamental technology for real-time vehicle detection and traffic counting on roads. In addition to these features, the system interfaces effortlessly with data pipelines and machine learning projects by storing gathered traffic data in CSV (Comma-Separated Values) format. The major goal of the project is to improve traffic management and analytics by reliably recognizing and counting automobiles on roads using YOLO v8, a cutting-edge object detection algorithm. Furthermore, the Intelligent Traffic Monitoring system solves the need for efficient data storage and exchange by including a data recording mechanism. The system saves pertinent traffic information, such as vehicle counts, timestamps, and maybe other relevant metadata, to a CSV file. This information is easily accessible and usable in downstream data pipelines, analytics, or machine learning applications for comprehensive traffic studies and urban planning. This system has been carefully evaluated, proving great accuracy in vehicle detection and traffic across a wide range of traffic circumstances. The CSV file output format assures compatibility and ease of integration with a variety of data processing tools, allowing for the seamless integration of traffic data into larger data-driven projects. Our unified architecture runs exceptionally fast. Our YOLO model can process photos in real-time at 45 frames per second. This study gives a full description of the Intelligent Traffic Monitoring project, including methodology, system architecture, and performance evaluation. By combining real-time vehicle detection, traffic counting, and efficient data logging, this project not only advances traffic management but also serves as a valuable resource for researchers and practitioners working on data-centric urban planning and transportation projects.
Keywords: CSV; Machine Learning; Real time vehicle; Traffic Inspector; YOLOv8