Highway Debris Detection Algorithm Based on Vehicle Trajectories

Highway debris is one of the main causes of highway accidents, so detecting highway debris is an important task for road safety. However, the problem is challenging because it involves a large number of factors, including the speed and direction of the vehicle, the condition of the roadway, and the type and location of the debris. In this paper, we propose a highway debris detection algorithm based on vehicle trajectories that combines deep learning and traditional methods. The algorithm segments the detection process into multiple modules. Firstly, the initialization module acquires video stream information and performs region of interest (ROI) labeling. Subsequently, the detection-tracking module is employed to detect and track high-speed vehicle targets in real-time. In the debris judgment module, two core algorithms are used: curvature-based judgment of sharp turns and lane change judgment based on linear fitting. These algorithms analyze the trajectory of the vehicle to infer the potential presence of debris. Finally, the alarm is triggered by the alarm message generation module to complete the detection of highway debris on the whole highway. After testing the actual road monitoring video, the algorithm is able to accurately determine the highway debris and make accurate identification and alarm, with the advantages of real-time, high efficiency and accuracy.