Traffic sign detection is to identify the location and category of traffic signs from images or videos in order to provide timely and accurate information for drivers, and has practical significance in early warning of road conditions and protecting driver safety. Aiming at the problems of large computation and high reference quantity in existing traffic sign detection algorithms, this paper proposes a lightweight traffic sign detection algorithm based on improved YOLOv7. The algorithm is divided into four parts: input, backbone network of feature extraction, neck network of feature fusion and head network of target prediction. In this paper, large kernel convolution is introduced into the backbone network, which increases the effective receptive field and improves the ability of feature extraction. In this paper, the detection of neck fusion coordinate attention, random pooling and other methods can not only build channel attention and capture accurate position, but also improve the generalization ability of the network. In addition, a comprehensive depth-separable convolution module is proposed to extract image features by reducing the number of parameters and the radical sign. Experimental results show that the detection accuracy of the proposed algorithm on the CCTSDB2021 data set reaches 93.13%, and mAP also reaches 87.59%, which is a great improvement compared with other methods of the same type. The network achieves a high accuracy rate under the condition of low parameter number and calculation amount, which can not only accurately capture the location information of traffic signs, but also achieve a high accuracy rate. At the same time, it can accurately predict the traffic signs.