针对小目标交通标志的错检和漏检导致识别网络精度下降的问题，提出了一种基于CGT-YOLO的小目标交通标志识别算法。首先，采用CAM上下文增强模块替代YOLOv5s网络中的 SPPF 模块，通过不同膨胀率的卷积操作增强小目标交通标志的特征表达。其次，在YOLOv5s主干网络的Concat模块后插入GAM全局注意力模块，提取CAM增强后的特征并通过三维置换、多层感知器及卷积空间注意力，增强通道与空间之间的全局交互，从而突出小目标交通标志的特征，解决复杂背景和远距离带来的负面影响。最后，通过TSC解耦头对分类和定位任务的特征进行解耦，分别生成语义丰富的低分辨率分类特征图和包含边界信息的高分辨率定位特征图，解决小目标交通标志分类与定位任务之间的特征冲突。实验结果表明，改进后的模型在各项指标上均有提升：漏报率和误报率分别降低12.1%和11.6%；mAP(0.5:0.95)提高2.6%。此改进显著减少错检与漏检，提升小目标交通标志的识别精度。

To address the issue of decreased recognition accuracy caused by false and missed detections of small target traffic signs, a small traffic sign recognition algorithm based on CGT-YOLO is proposed. First, the Context Enhancement Module is introduced to replace the SPPF module in the YOLOv5s network, enhancing the feature representation of small traffic signs through convolutions with varying dilation rates. Next, the Global Attention Module is added after the Concat module in the YOLOv5s backbone network. The GAM extracts the CAM-enhanced features and further strengthens the global interaction between channels and spatial dimensions using 3D permutation, multi-layer perceptron, and convolutional spatial attention. This process highlights the features of small target traffic signs and mitigates the negative effects of complex backgrounds and long distances. Finally, the TSC decoupling head is utilized to separate the features for classification and localization tasks. This module generates semantically rich, low-resolution classification feature maps and high-resolution localization maps containing boundary information, effectively resolving the feature conflicts between the two tasks. Experimental results show that the improved model has improved in all indicators: the miss rate and false positive rate decreased by 12.1% and 11.6%, respectively, and the mAP(0.5:0.95) increased by 2.6%. These improvements effectively reduce false positives and false negatives, significantly, enhancing the recognition accuracy of small traffic signs.