LE-YOLOv5: A Lightweight and Efficient Road Damage Detection Algorithm Based on Improved YOLOv5
文献类型：期刊论文
| 作者 | Diao, Zhuo4; Huang, Xianfu2,3 ; Liu, Han1; Liu, Zhanwei4; Huang XF(黄先富)
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| 刊名 | INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
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| 出版日期 | 2023-09-28 |
| 卷号 | 2023页码:17 |
| ISSN号 | 0884-8173 |
| DOI | 10.1155/2023/8879622 |
| 通讯作者 | Liu, Zhanwei(liuzw@bit.edu.cn) |
| 英文摘要 | 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. |
| 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 |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001077373200001 |
| 资助机构 | 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 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/93105]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 通讯作者 | Liu, Zhanwei |
| 作者单位 | 1.Beijing Inst Struct & Environm Engn, Beijing 100076, Peoples R China 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, State Key Lab Nonlinear Mech LNM, Inst Mech, Beijing 100190, Peoples R China 4.Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China |
| 推荐引用方式 GB/T 7714 | Diao, Zhuo,Huang, Xianfu,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,Huang, Xianfu,Liu, Han,Liu, Zhanwei,&黄先富.(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. |
入库方式： OAI收割
来源：力学研究所
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