Under foggy condition,the image quality is low,which makes object detection difficult.The traditional YOLOv5s algorithm can detect in foggy environment,but the detection speed is slow.In this regard,a foggy target detection algorithm named YOLO-FOG was proposed,which can improve the detection speed in foggy environment.This algorithm uses the RepVGG structure in the backbone network to reduce the amount of computation,improve the feature representation ability,and accelerate the inference speed,so as to improve the real-time detection performance.The experimental result shows that for the RTTS dataset,the average accuracy of the five types of targets such as bicycle,bus,car,motorcycle and pedestrian can reach 81.72％,79.99％,89.24％,73.46％and 83.34％respectively,and the recognition time is only 0.065 s per sheet.The YOLO-FOG foggy target detection algorithm has both accuracy and real-time performance,and has a good application prospect.
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