Real-Time Detection Algorithm for Small-Target Traffic Signs Based on Improved YOLOv5

被引：2

作者：

Hu, Junping ^{[1
]}

Wang, Hongshu ^{[1
]}

Dai, Xiaobiao ^{[2
]}

Gao, Xiaolin ^{[3
]}

机构：

[1] College of Mechanical and Electrical Engineering, Central South University, Changsha,410083, China

[2] School of Mechanical and Energy Engineering, Shaoyang University, Hunan, Shaoyang,422000, China

[3] Collaborative Innovation Centre, Jiangxi University of Technology, Nanchang,330098, China

来源：

Computer Engineering and Applications | 2024年 / 59卷 / 02期

关键词：

Extraction - Object detection - Semantics - Signal detection - Traffic signs;

D O I：

10.3778/j.issn.1002-8331.2206-0503

中图分类号：

学科分类号：

摘要：

Accurate real-time detection of small target traffic signs in real scenarios is important for autonomous driving. To address the problem of low accuracy of YOLOv5 algorithm in detecting small target traffic signs, a real-time detection algorithm for small target traffic signs based on improved YOLOv5 is proposed. Drawing on the idea of cross-stage local networks, another gradient path is set on the spatial pyramid pooling of YOLOv5 to strengthen the feature extraction capability; the learnable adaptive weights of deep and shallow convolutional features are added to the neck feature fusion to better fuse deep semantic and shallow detail features and improve the detection accuracy of small target traffic signs. To verify the superiority of the proposed algorithm, experimental validation is carried out on the TT100K traffic sign dataset. The experimental results show that the mean average precision（mAP）of the proposed algorithm on small-target traffic signs is 77.3%, which is 5.4 percentage points better than the original YOLOv5, and also outperforms the detection results of SSD, RetinaNet, YOLOX and Swin Transformer. The proposed algorithm runs at 46.2 frame/s, meeting the requirements for real-time detection. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press

引用

页码：185 / 193