Abstract: Traffic sign detection technology is an essential part of the advanced driving assistance system. The real-life driving environment requires the traffic sign detection system to have an extremely high real-time performance and accuracy. Lightweight network MobileNetv2-SSD can satisfy real-time detection tasks, but the accuracy can not satisfy the actual requirement. This paper takes MobileNetv2-SSD as the underlying network, proposed a multi-scale pixel feature fusion method based on pixel shuffle, and introduced an efficient channel attention mechanism at the network's detection layer to achieve feature enhancement. The proposed method effectively improves the detection performance of small traffic signs while ensuring real-time performance. Experimental results show that the algorithm model in this paper can detect traffic signs in real environment accurately and in real-time. On the CSUST Chinese traffic sign detection benchmark (CCTSDB), our model obtained 93.2% mAP with the only 17.3M model size, and 0.022 seconds for detecting each image.