{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:44:04Z","timestamp":1772207044577,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T00:00:00Z","timestamp":1736726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shandong Province Science and Technology smes Innovation Ability Improvement Project","award":["2024TSGC0158"],"award-info":[{"award-number":["2024TSGC0158"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In contemporary society, fatigue driving is a major cause of traffic accidents, making accurate and timely detection critical for improving driving safety. In this study, we propose a novel fatigue detection model, CSA-YOLO, designed to enhance the accuracy and efficiency of facial fatigue detection. The model is based on the YOLOv9s network model and introduces several key improvements to address the limitations of traditional methods, which often lose critical edge information. First, the Cross-Stage Partial Network (C3 module) replaces the RepNCSPELAN4 module to enhance the model\u2019s ability to extract edge information effectively. Second, the incorporation of the SimAM attention mechanism improves feature selection, while the Content-Aware ReAssembly of Features (CARAFE) upsampling operator enhances the quality of reconstructed image details. Experimental results demonstrate that the proposed CSA-YOLO model achieves significant performance improvements, with a 2.24% increase in accuracy, a 2.58% improvement in recall, and a 2.44% boost in mAP compared to the original YOLOv9s model. These results highlight the model\u2019s potential for practical application in reducing the risks of fatigue-related accidents.<\/jats:p>","DOI":"10.3390\/sym17010111","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T04:01:52Z","timestamp":1736740912000},"page":"111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Face Fatigue Detection Model for Edge Information Extraction"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6816-372X","authenticated-orcid":false,"given":"Ge","family":"Wang","sequence":"first","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6000-6128","authenticated-orcid":false,"given":"Fangqian","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Sang","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuteng","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Han","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Technology Department, Shandong Xinhuaan Information Technology Co., Ltd., Qingdao 266041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e011461","DOI":"10.1136\/bmjopen-2016-011461","article-title":"A UK survey of driving behaviour, fatigue, risk taking and road traffic accidents","volume":"6","author":"Smith","year":"2016","journal-title":"BMJ Open"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sayedelahl, M. 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