{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:30:26Z","timestamp":1779294626920,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shandong Province Higher Education Young Innovative Team Project","award":["2022KJ037"],"award-info":[{"award-number":["2022KJ037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Considering the high incidence of accidents at tunnel construction sites, using robots to replace humans in hazardous tasks can effectively safeguard their lives. However, most robots currently used in this field require manual control and lack autonomous obstacle avoidance capability. To address these issues, we propose a lightweight model based on an improved version of YOLOv5 for obstacle detection. Firstly, to enhance detection speed and reduce computational load, we modify the backbone network to the lightweight Shufflenet v2. Secondly, we introduce a coordinate attention mechanism to enhance the network\u2019s ability to learn feature representations. Subsequently, we replace the neck convolution block with GSConv to improve the model\u2019s efficiency. Finally, we modify the model\u2019s upsampling method to further enhance detection accuracy. Through comparative experiments on the model, the results demonstrate that our approach achieves an approximately 37% increase in detection speed with a minimal accuracy reduction of 1.5%. The frame rate has improved by about 54%, the parameter count has decreased by approximately 74%, and the model size has decreased by 2.5 MB. The experimental results indicate that our method can reduce hardware requirements for the model, striking a balance between detection speed and accuracy.<\/jats:p>","DOI":"10.3390\/s24020395","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T03:36:58Z","timestamp":1704771418000},"page":"395","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Lightweight Tunnel Obstacle Detection Based on Improved YOLOv5"],"prefix":"10.3390","volume":"24","author":[{"given":"Yingjie","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanyi","family":"Ma","sequence":"additional","affiliation":[{"name":"Shandong High-Speed Group Co., Ltd., Jinan 250014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Qilu Transportation, Shandong University, Jinan 250100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Qilu Transportation, Shandong University, Jinan 250100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zizheng","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Qilu Transportation, Shandong University, Jinan 250100, China"},{"name":"Jiangsu XCMG State Key Laboratory Technology Co., Ltd., Xuzhou 221004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105419","DOI":"10.1016\/j.ijrmms.2023.105419","article-title":"Development and application of an intelligent robot for rock mass structure detection: A case study of Letuan tunnel in Shandong, China","volume":"169","author":"Qin","year":"2023","journal-title":"Int. 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