太原理工大学学报2024，Vol.55Issue(1)：195-203,9.DOI:10.16355/j.tyut.1007-9432.2023BD009
改进YOLOv7的轻量化交通标志检测算法
A Lightweight Traffic Sign Detection Algorithm Based on Improved YOLOv7
摘要
Abstract
[Purposes]Aiming at the problems of large computation and high reference quanti-ty 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 net-work,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 a-bility of the network.In addition,a comprehensive depth-separable convolution module was pro-posed to extract image features by reducing the number of parameters and the radical sign.[Re-sults]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 im-provement 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 ac-curately 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.关键词
交通标志检测/轻量化/大核卷积/坐标注意力/深度可分离卷积Key words
traffic sign detection/lightweight/large kernel convolution/coordinate attention/depthwise separable convolution分类
信息技术与安全科学引用本文复制引用
李禹纬,付锐,刘帆..改进YOLOv7的轻量化交通标志检测算法[J].太原理工大学学报,2024,55(1):195-203,9.基金项目
国家自然科学基金资助项目(61703299,62102279) （61703299,62102279）
