YOLO-TS: A Lightweight YOLO Model for Traffic Sign Detection

Existing traffic sign detection algorithms suffer from high computational complexity and large parameter sizes, limiting their deployability. The YOLO-TS model integrates the Normalized Wasserstein Distance (NWD) with the Complete Intersection over Union (CIoU) loss function, significantly enhancing the detection of small traffic signs. The integration of StarBlock from StarNet into the C2f module forms the C2f-Star configuration, which simplifies the architecture. In addition, the Slimneck design paradigm is introduced into the neck network to further reduce computational demands while maintaining model accuracy. Subsequently, a Squeeze and Excitations Shared Detection Head (SESDH) is developed to integrate a squeezing and excitation attention mechanism. This design helps diminish the intricacy of the network architecture, increase attention on areas where traffic signs are presence and improve the ability of the model to indicate objects in complex surroundings. Experimental results on the CCTSDB traffic sign dataset reveal that the updated algorithm decreased the number of parameters by 35.33%, reduced the computation by 35.80% and shrunk the model size by 32.94% of the baseline YOLOv8n model while improving the mAP@50 by 1%, and the F1 score by 1.32%. Compared to other algorithms, here, a good balance between accuracy and lightweight design is achieved.