Research on target detection algorithm for foggy weather based on YOLO-FOG

Images acquired under foggy conditions have blurred lighting and low resolution and contrast, while the feature extraction of traditional target detection designs does not take into account the fog obscuring the target, affecting the recognition rate of targets such as vehicles and pedestrians. Based on this, this paper proposes YOLO-FOG. Based on Anchor-Free, the TSCODE module is inserted to further separate the feature encoding of the two tasks; MobileViT is introduced into the pyramid structure of the feature graph to construct the long-distance dependencies of the features and integrate the features. When evaluated using the RTTS dataset, the mAP values of Bicycle, Bus, Car, Motorbike, and Person with their five types of targets can reach 82.19%, 81.36%, 89.47%, 75.89%, and 84.01%, respectively, and meanwhile the simultaneous detection speed up to 65.52 fps, which proves that this paper's method combines both accuracy and speed, and has good application prospects.