Automatic detection of traffic signs is crucial for improving the robustness of environmental perception in autonomous vehicles. In real-world scenarios, traffic signs are often occluded, resulting in issues such as complex background interference, small target scale and incomplete sign information, which degrade detection performance. To address the challenge of occluded traffic signs in complex backgrounds, we propose a feature extraction method C2f_color based on color correlogram, which leverages the prominent and stable color features of traffic signs to enhance their distinguishability in challenging scenes. For small-scale targets and low-resolution problems, we introduce a feature enhancement module C2SEAM that integrates a self-ensembling multi-scale attention mechanism (SEAM), which improves the model′s ability to extract fine-grained features across multiple scales and enhances detection accuracy for small objects and low-resolution samples. Additionally, to handle missing sign information due to occlusion, we propose contextmixing convolution(ConMix), which dynamically fuses contextual information to compensate for missing features and improve the representation of occluded traffic signs. Experiments on the CCTSDB public dataset and our custom traffic sign dataset under occlusion and complex conditions (TSOCC) show that our method improves mAP@0.5 by 8.4% and 3.5%, respectively, while P increases by 0.9% and 6.2%, respectively, compared with the baseline model.