YOLO-TSF: A Small Traffic Sign Detection Algorithm for Foggy Road Scenes

The accurate and rapid detection of traffic signs is crucial for intelligent transportation systems. Aiming at the problems that traffic signs have including more small targets in road scenes as well as misdetection, omission, and low recognition accuracy under the influence of fog, we propose a model for detecting traffic signs in foggy road scenes-YOLO-TSF. Firstly, we design the CCAM attention module and combine it with the idea of local-global residual learning thus proposing the LGFFM to enhance the model recognition capabilities in foggy weather. Secondly, we design MASFFHead by introducing the idea of ASFF to solve the feature loss problem of cross-scale fusion and perform a secondary extraction of small targets. Additionally, we design the NWD-CIoU by combining NWD and CIoU to solve the issue of inadequate learning capacity of IoU for diminutive target features. Finally, to address the dearth of foggy traffic signs datasets, we construct a new foggy traffic signs dataset, Foggy-TT100k. The experimental results show that the mAP@0.5, mAP@0.5:0.95, Precision, and F1-score of YOLO-TSF are improved by 8.8%, 7.8%, 7.1%, and 8.0%, respectively, compared with YOLOv8s, which proves its effectiveness in detecting small traffic signs in foggy scenes with visibility between 50 and 200 m.