The Smart Traffic Management System is a project aimed at developing an intelligent traffic management system using computer vision, machine learning, and algorithmic optimization techniques. The system consists of three main modules: Vehicle Detection, Signal Switching Algorithm, and Visualization.
The Vehicle Detection Module utilizes YOLOv7, a state-of-the-art object detection model, to detect vehicles in real-time from camera feeds. It processes images or video streams, identifies vehicles, and provides data on the number and types of vehicles present.
The Signal Switching Algorithm dynamically adjusts traffic signal timings based on inputs from the Vehicle Detection Module and other factors such as traffic flow, lane configurations, and historical data. It aims to optimize traffic flow, minimize congestion, and reduce waiting times at intersections.
The Visualization Module uses the Pygame library to provide a graphical user interface (GUI) for visualizing the traffic signal switching process. It displays the current state of traffic signals, vehicle detection results, and any relevant information to aid in monitoring and analysis.
- Real-time vehicle detection using YOLOv7.
- Dynamic adjustment of traffic signal timings based on traffic conditions.
- Graphical visualization of traffic signal switching process using Pygame.
- Optimized traffic flow, reduced congestion, and minimized waiting times at intersections.
- Built using Python, OpenCV, Pygame, and YOLOv7 for vehicle detection.
