Intelligent Traffic Signal Optimization Using Image Processing And Canny Edge Detection For Density-Based Traffic Management

Traffic congestion has become a major challenge in urban transportation systems due to the increasing number of vehicles on roads. Conventional traffic signal systems generally operate on fixed timers, which often results in inefficient traffic management and unnecessary waiting time at intersections. To address this issue, an intelligent traffic control system based on image processing techniques is proposed. The system captures real-time traffic images using surveillance cameras and processes them to estimate vehicle density. The captured images undergo preprocessing operations such as grayscale conversion and noise reduction before applying the Canny edge detection algorithm to identify vehicle edges. The density of vehicles is determined by calculating the number of edge pixels in the processed image and comparing them with a reference image. In addition, the You Only Look Once (YOLO) object detection algorithm is used to identify emergency vehicles such as ambulances and provide them with priority signal allocation. Based on the estimated traffic density, the system dynamically adjusts traffic signal duration for each lane. The proposed approach improves traffic flow efficiency, reduces waiting time, and enhances emergency vehicle movement at intersections. This intelligent system can serve as a practical solution for modern smart city traffic management.