A Lightweight Traffic Sign Detection Algorithm Based on Improved YOLOv7

Abstract

Purposes Aiming at the problems of large computation and high reference quantity in existing traffic sign detection algorithms, in this paper we proposed a lightweight traffic sign detection algorithm based on improved YOLOv7. Methods The algorithm is divided into four parts: input, backbone network of feature extraction, neck network of feature fusion, and head network of target prediction. Large kernel convolution was introduced into the backbone network, which increases the effective receptive field and improves the ability of feature extraction. 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 was proposed to extract image features by reducing the number of parameters and the radical sign. Results 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 with respect to 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.

Keywords

• traffic sign detection
• lightweight
• large kernel convolution
• coordinate attention
• depthwise separable convolution