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

• DOI: 10.3390/electronics13183744
• Publication Date: 2024-09-20T14:49:48Z
• Authors: Rongzhen Li; Yajun Chen; Yu Wang; Chaoyue Sun

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