Published Paper Details:

The intelligent traffic management system uses the air to improve traffic flow in the city and reduce traffic congestion by processing images and deep learning. The system performs preprocessing operations such as resizing, grayscale conversion, etc. by preprocessing the vehicle image in the configuration file. The main features are extracted using the metric and local binary model (LBP). These images are divided into training and test sets for developing and evaluating the vehicle evaluation model using YOLO. The traffic is calculated as the number of vehicles per unit length and sent to the cloud server for storage. The server uses a signal switching algorithm to calculate the time it takes for traffic to see the green light. The system's performance is measured by indicators such as accuracy and error, and the results are presented in comparison tables and tables. The web API also allows users to register, log in, upload images, and view results, increasing accessibility and user engagement.