Improved foggy pedestrian detection algorithm based on YOLOv5s

Abstract

To address the problem of low detection accuracy of YOLOv5s target detection algorithm in foggy traffic environment, an improved YOLOv5s-based pedestrian detection algorithm for foggy skies is proposed. The algorithm uses image defogging techniques to preprocess the data, expands the sample size by manually generating the Foggy Cityscapes-Person dataset through a new fog simulation pipeline algorithm, and enhances the network's ability to sense small targets under foggy skies by adjusting the loss function and the training method to improve the detection accuracy of pedestrians under foggy skies, resulting in an increase of the mAP value from 64.97% to The mAP value increases from 64.97% to 81.29%. The experimental results show that the YOLOv5s-ACE network model proposed in this paper effectively reduces the missing detection rate and false detection rate, and the model can quickly and accurately detect pedestrian targets in foggy sky scenes.