A system for combining Dehazer weather processing with AI traffic light technology enforcement

This study proposes an integrated roadside system that combines image enhancement for adverse weather conditions with artificial intelligence (AI) technology enforcement at intersections. It aims to address critical issues in traffic management, such as accidents and congestion. The AI optimizes road safety for users, reduces traffic violations, and employs improved methods to control vehicle speed and direction, thereby minimizing accidents and congestion. Furthermore, the system leverages Convolutional Neural Network (CNN) based model called All in One Dehazing Network (AOD-Net) used to enhance detection performance under specific conditions, and the YOLOv7 technique accurately identifies vehicles. With these model, the intelligent roadside system can sensitively detect vehicle behavior on the road, better coordinate traffic flow, improve road efficiency, reduce traffic congestion, and decrease the occurrence of traffic accidents. Overall, this intelligent roadside system with AI-powered traffic signal lights is expected to play a vital role in urban traffic management, effectively reducing traffic accidents and congestion, enhancing road efficiency, and providing a safer and more convenient travel experience for the public.