Automated Detection of Roadway and Traffic Control Conditions Using On-Board Image Processing

Accurate detection of roadway and traffic control conditions is crucial for enhancing Advanced Driver Assistance Systems (ADAS) in dynamic environments. This study aims to leverage deep learning techniques, specifically YOLO series, for their identification in real-world scenarios. Detection of traffic signs, markings and signal along with road conditions such as potholes, and speed breakers are considered. The YOLO models perform well and demonstrates effective and real-time detection capacities.