In view of the flaws of low recognition accuracy and incomplete detection of traffic signs in existing target detection algorithms, a traffic sign detection algorithm has thus been proposed with an attention mechanism incorporated into YOLO11n. Firstly, the convolution and attention fusion module (CAFM) is integrated with the YOLO11n backbone, so as to effectively model both global and local features of images by combining convolution operations with attention mechanisms to enhance detection accuracy. Secondly, by incorporating the global attention mechanism module into the YOLO11n neck, the model is enabled to extract semantic and positional information from features more fully, thereby improving the feature expression ability of the model. Finally, a small target detection layer is added to retain more shallow detail information to enhance the fusion of deep and shallow semantic information, thus overcoming the incomplete detection of small targets. The experimental results show that the improved algorithm is characterized with a good accuracy, recall, and mean average precision (mAP) of 83.9%, 70.7%, and 82.4%, respectively, in the TT100K dataset. Compared with the original model YOLO11n, it improves by 5.7, 2.7, and 6.3 percentage points, verifying the effectiveness of the improvement.