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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\u2010YOLOv5 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\u2010stage partial network module that was designed using the one\u2010time aggregation method. Specifically, we propose a parameter\u2010free attentional feature fusion (PAFF) module, which significantly enhances the model\u2019s 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\u2010means 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\u2010YOLOv5 proposed in this document can stably and effectively detect road damage. Compared to YOLOv5s, LE\u2010YOLOv5 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\u2010YOLOv5 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\u2010YOLOv5 exhibits excellent robustness and reliability in complex road environments.<\/jats:p>","DOI":"10.1155\/2023\/8879622","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T20:20:07Z","timestamp":1695932407000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["LE\u2010YOLOv5: A Lightweight and Efficient Road Damage Detection Algorithm Based on Improved YOLOv5"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5544-3519","authenticated-orcid":false,"given":"Zhuo","family":"Diao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianfu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5318-956X","authenticated-orcid":false,"given":"Zhanwei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106062"},{"key":"e_1_2_10_2_2","doi-asserted-by":"crossref","unstructured":"GirshickR. 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