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A traffic sign detection method that combines attention mechanism and context information is proposed on the basis of YOLOv3, the method is proposed to address the problems of low accuracy of small targets, low real-time detection and missing target detection in current traffic sign detection. In this method, firstly, the channel of feature graph is re-calibrated by improving the compression method of channel attention mechanism, while the channel weight of less information is suppressed； then the spatial pyramid pooling module SPP is introduced to obtain multi-scale local information; finally, the feature mapping is added and spliced into the small target part of the original feature fu-sion network. The contextual information is fully used to enhance the detection of small targets. The experimental results show that on the TT100K (Tsinghua-Tencent 100K) traffic sign dataset, the improved network can detect targets more effectively compared with the origi-nal YOLOv3 network; with little change in frames per second (FPS), the average precision mean and the small target The average accuracy mean and small target mean were improved by 3.03% and 4.59%, respectively. The experimental results demonstrate the effectiveness of the improved network in small target detection and overall detection.