Robust and Efficient Traffic Monitoring System Under Adverse Weather

Recent advancements in object detection and deep learning have significantly enhanced intelligent transportation systems, contributing to safer and more efficient roadways. This study proposes a comprehensive framework that leverages YOLOv8, augmented with vehicle tracking and a dehazing module, to detect and track moving vehicles under various weather conditions. The system can accurately identify vehicle types such as cars, motorcycles, buses, or trucks, track their trajectories, and estimate their speeds. It includes alert mechanisms that notify when vehicles exceed or fall below speed limits or travel in the wrong direction. To ensure robustness in adverse weather, the framework incorporates a hybrid loss function with both pixel and structure similarity measurements to train the dehazing model. This dehazing model is capable of effectively mitigating the effects of haze, fog, and rain in video streams. This work highlights the potential of AI-driven solutions for real-time vehicle monitoring, risk mitigation, and the advancement of road safety in adverse weather.