A Real-Time Traffic Sign Detection Algorithm Based on Improved YOLO11n

To address the issues of low detection accuracy and high miss rates in long-range small traffic sign detection, which are caused by insufficient feature information and susceptibility to background interference, this paper proposes an improved real-time traffic sign detection algorithm based on YOLO11n. First, a cross-guided feature extraction module, C3k2_CGPEMA, is designed within the neck network. By embedding the Efficient Multi-Scale Attention (EMA) mechanism into the feature extraction branch of Partial Convolution (PConv), this module utilizes the spatial attention mask generated by the convolutional branch to provide cross-branch guidance and filter out complex background noise from the identity branch. This achieves precise fine-grained feature focusing while preserving high-frequency spatial details. Furthermore, a joint bounding box regression loss function combining Complete Intersection over Union (CIoU) and Gaussian Combined Distance (GCD) is adopted. This preserves the stable convergence properties of CIoU while leveraging the scale invariance of GCD to enhance the regression accuracy for small targets. Finally, the detection layers are reconstructed by removing the P5 layer and introducing a high-resolution P2 layer (160 × 160), significantly strengthening the localization capability for distant, tiny targets. Experimental results demonstrate that the proposed algorithm achieves improvements of 5.4, 7.4, and 6.6 points in precision, recall, and mAP@0.5, respectively, on the TT100K dataset compared to the baseline YOLO11n. While boosting detection accuracy, the model maintains an inference speed of 114.5 frames per second (FPS), fully satisfying the requirements for real-time detection in in-vehicle environments. Generalization experiments conducted on the CCTSDB dataset further validate the robustness of the proposed algorithm in complex environments.