LE-YOLOv5: A Lightweight and Efficient Road Damage Detection Algorithm Based on Improved YOLOv5

doi:10.1155/2023/8879622

IMECH-IR > 非线性力学国家重点实验室

	LE-YOLOv5: A Lightweight and Efficient Road Damage Detection Algorithm Based on Improved YOLOv5
	Diao, Zhuo; Huang XF(黄先富); Liu, Han; Liu, Zhanwei
通讯作者	Liu, Zhanwei(liuzw@bit.edu.cn)
发表期刊	INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
	2023-09-28
卷号	2023 页码:17
ISSN	0884-8173
摘要	Road damage detection is very important for road safety and timely repair. The previous detection methods mainly rely on humans or large machines, which are costly and inefficient. Existing algorithms are computationally expensive and difficult to arrange in edge detection devices. To solve this problem, we propose a lightweight and efficient road damage detection algorithm LE-YOLOv5 based on YOLOv5. We propose a global shuffle attention module to improve the shortcomings of the SE attention module in MobileNetV3, which in turn builds a better backbone feature extraction network. It greatly reduces the parameters and GFLOPS of the model while increasing the computational speed. To construct a simple and efficient neck network, a lightweight hybrid convolution is introduced into the neck network to replace the standard convolution. Meanwhile, we introduce the lightweight coordinate attention module into the cross-stage partial network module that was designed using the one-time aggregation method. Specifically, we propose a parameter-free attentional feature fusion (PAFF) module, which significantly enhances the model's ability to capture contextual information at a long distance by guiding and enhancing correlation learning between the channel direction and spatial direction without introducing additional parameters. The K-means clustering algorithm is used to make the anchor boxes more suitable for the dataset. Finally, we use a label smoothing algorithm to improve the generalization ability of the model. The experimental results show that the LE-YOLOv5 proposed in this document can stably and effectively detect road damage. Compared to YOLOv5s, LE-YOLOv5 reduces the parameters by 52.6% and reduces the GFLOPS by 57.0%. However, notably, the mean average precision (mAP) of our model improves by 5.3%. This means that LE-YOLOv5 is much more lightweight while still providing excellent performance. We set up visualization experiments for multialgorithm comparative detection in a variety of complex road environments. The experimental results show that LE-YOLOv5 exhibits excellent robustness and reliability in complex road environments.
DOI	10.1155/2023/8879622
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:001077373200001
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
资助项目	This work was financially supported by the National Natural Science Foundation of China (grant no. 11972084) and the National Natural Science Foundation of China (grant no. 12372178).[12372178] ; National Natural Science Foundation of China
项目资助者	This work was financially supported by the National Natural Science Foundation of China (grant no. 11972084) and the National Natural Science Foundation of China (grant no. 12372178). ; National Natural Science Foundation of China
论文分区	二类/Q1
力学所作者排名	2
RpAuthor	Liu, Zhanwei
引用统计
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/93106
专题	非线性力学国家重点实验室
推荐引用方式 GB/T 7714	Diao, Zhuo,Huang XF,Liu, Han,et al. LE-YOLOv5: A Lightweight and Efficient Road Damage Detection Algorithm Based on Improved YOLOv5[J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS,2023,2023:17.
APA	Diao, Zhuo,黄先富,Liu, Han,&Liu, Zhanwei.(2023).LE-YOLOv5: A Lightweight and Efficient Road Damage Detection Algorithm Based on Improved YOLOv5.INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS,2023,17.
MLA	Diao, Zhuo,et al."LE-YOLOv5: A Lightweight and Efficient Road Damage Detection Algorithm Based on Improved YOLOv5".INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2023(2023):17.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Jp2023A012.pdf（5584KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载