In the application of traffic sign recognition, most of the targets to be detected are small targets, which are prone to problems such as missed detection and false detection. In order to solve these problems, an improved traffic sign recognition algorithm, FKDS-YOLOv8s, was designed based on the YOLOv8s algorithm. FasterBlock is used to reconstruct the C2f module to form a new lightweight module C2f-Faster, which not only improves the feature extraction ability of the model, but also reduces the computational overhead. Based on the SENet and ResNeXt models, a new detection head Detect_SR was designed to enable the model to effectively focus on the key features of small targets. DySample, a lightweight and efficient dynamic upsampler, significantly reduces GPU memory consumption. By increasing the output level of upsampling and prediction, the model can capture rich position information, which effectively solves the problem of insufficient information when the YOLOv8s model processes small targets. The Shape-