Vehicle Detection Based on YOLOv3 in Adverse Weather Conditions

The target detection algorithm is mainly affected by multiple factors such as illumination, clarity, overlap, target size, detection accuracy, and speed decrease greatly in adverse weather conditions. In order to solve these problems, we test the detection performance of YOLOv3 for pedestrians and vehicles with fogged photos. The fogging method is based on the airlight model. The results are shown that the YOLOv3 algorithm can be used for pedestrian and vehicle detection in haze environments, which may be a useful guideline for developing and improving related traffic safety detection systems.