Deep Learning-based Traffic Estimator: YOLOv5 with Historical Data

This work uses the YOLOv5 model to propose a deep learning-based traffic vehicle detection system. To recognize different vehicle categories, such as automobiles, number plates, two-wheelers, vehicles, buses, and trucks, the system analyses surveillance data. With labels and images arranged for easy processing, the dataset is prepared for YOLOv5 training. The model can efficiently learn car features thanks to a configuration file that specifies class labels. After that, test photos are used to assess the trained model and extract vehicle counts for traffic pattern analysis. Preprocessing procedures make guarantee that the image-label mapping is correct, which improves detection accuracy. By estimating the average number of vehicles over historical data, traffic density estimation facilitates flow analysis and congestion evaluation. These findings help with urban planning and real-time traffic monitoring. The suggested method reduces manual labour in traffic monitoring by automating vehicle recognition. It improves traffic safety and law enforcement by enabling real-time multi-class detection. This Traffic Density is then used to evaluate the congestion levels of a road in real time.