Smart Traffic Monitoring System using YOLO and Deep Learning Techniques

Abstract

As the world's population grows, there are more vehicles on the road every day, which leads to an increase in heavy traffic. Traffic monitoring is essential for preventing accidents. To detect reckless drivers and other traffic infractions, a model that can track, identify, and categorize vehicles is needed. The task of counting the number of vehicles is crucial in traffic situations because it allows the authorities to prevent accidents and traffic jams caused by heavy traffic. The approach outlined in the study uses the image processing methods YOLO and OpenCV to count the number of vehicles, classify them, and identify them. By processing the images from the input video given to OpenCV, a software library, the objects are detected and identified. In comparison to other object detection algorithms, the real-time object detection algorithm YOLO is both quicker and more accurate. The accuracy and efficiency of vehicle detection and classification have been greatly enhanced by convolutional neural networks and other machine learning algorithms, enabling real-time analysis of enormous amounts of data. With the help of this technology, driving safety will be increased, traffic flow will be optimized, and autonomous driving will be made possible.