Vehicle Detection based on Yolov5 in Typical Complex Weather Conditions

Traffic events can be carried out by traffic surveillance cameras and are paramount in ITS. Adverse weather conditions restrict the camera’s function by reducing the qualities of videos and images and increase the probability of the false detection to target vehicles. So, the research for vehicle detection under typical complex weather conditions is crucial for the development of ITS. A vehicle detection methodology composed with image enhancement+Yolov5 network under typical complex weather conditions is developed in this paper. First, the specified three image enhancement methods are researched, and their efficacy is evaluated by objective evaluation methods to adverse weather conditions. After that, appropriate enhancement algorithms are chosen for different degraded images, the enhanced images are put into Yolov5 network for training and the high detection precision has achieved on the validation dataset. Then, the comparison research is performance among Faster R-CNN, YoLov3 and the methodology proposed in this paper. It is found that the methodology has higher detection precision and lower time cost, and offers a better balance between the accuracy of detection and the velocity of execution in the three approaches.