With the rapid development of intelligent transportation systems, traffic sign detection plays a crucial role in ensuring driving safety and preventing accidents. However, detecting small traffic signs in complex road environments remains a significant challenge due to issues such as low resolution, dense distribution, and visually similar background interference. Existing methods face limitations including high computational cost, inconsistent feature alignment, and insufficient resolution in detection heads. To address these challenges, we propose the Traffic Sign Detection Network (TSD-Net), an improved framework designed to enhance the detection performance of small traffic signs in complex backgrounds. TSD-Net integrates a Feature Enhancement Module (FEM) to expand the network’s receptive field and enhance its capability to capture target features. Additionally, we introduce a high-resolution detection branch and an Adaptive Dynamic Feature Fusion (ADFF) detection head to optimize cross-scale feature fusion and preserve critical details of small objects. By incorporating the C3k2 module and dynamic convolution into the network, the framework achieves enhanced feature extraction flexibility while maintaining high computational efficiency. Extensive experiments on the TT100K benchmark dataset demonstrate that TSD-Net outperforms most existing methods in small object detection and complex background handling, achieving 91.4 mAP and 49.7 FPS on 640 × 640 low-resolution images, meeting the requirements of practical applications.