In order to solve the problems of low accuracy and large number of parameters in traffic sign detection, this paper proposes an improved SC-YOLOv8 traffic sign detection algorithm based on YOLOv8s. This algorithm uses the downsampling Adown module to replace the ordinary downsampling Conv, improving the model"s perception ability of the target; Replace the Bottleneck in C2f with the SCConv module and design a brand new C2f-SC module, significantly reducing model parameters; Adding a 160 × 160 scale detection head and removing a 20 × 20 scale detection head, effectively improving detection accuracy; Finally, the idea of using WIoU loss function is used to improve MPDIoU, replacing the original CIoU loss function with Wise-MPDIoU, alleviating the problem of imbalanced positive and negative samples. The algorithm was validated on the TT100K traffic sign dataset, and compared with the original model YOLOv8s, the accuracy P increased by 4.8%, the recall R increased by 6.7%, the mAP50 increased by 6.6%, and the parameter count Params decreased by 61.5%. Proved the effectiveness of the improvements made.