To address the challenges of insufficient feature representation, strong background interference and low localization accuracy in small traffic sign detection, an improved multi-scale feature enhancement algorithm TSD-YOLOs based on YOLOv11 is proposed. Firstly, a CSP-MSFPF module is designed using heterogeneous partial convolutions and cross-scale feature fusion, effectively enhances the feature expression of small targets. Secondly, a foreground enhancement feature pyramid network is constructed in the neck of the network to improve target focus and suppress background noise through contextual correlation and foreground enhancement. In addition, the Haar wavelet downsampling is adopted to retain high-frequency details while reducing computational overhead, alleviating information loss for small targets. Meanwhile, the WIoU v3 loss function is utilized to dynamically adjust gradient allocation and optimize sample distribution, improving localization accuracy. Finally, a channel pruning strategy based on the LAMP score is applied to compress the improved model, which significantly reduces model complexity and computation while maintaining detection performance. Experimental results demonstrate that compared to the baseline model, TSD-YOLOs achieves 2.6% and 2.1% improvements in mAP50 and mAP50:95 respectively on the TT100k dataset, with a detection rate of 242 fps; On the CCTSDB dataset, mAP50 and mAP50:95 improved by 1.6% and 3.9%, respectively. Furthermore, the computational cost is reduced by 27.6%, the model size is compressed by 58.7%, and the number of parameters is reduced by 59.6%. The experimental results adequately verify that TSD-YOLOs significantly improves the detection accuracy and robustness of small-target traffic signs in complex scenes while ensuring real-time performance.