{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:22:39Z","timestamp":1786116159258,"version":"build-2736575974"},"reference-count":39,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008238","name":"Central Guidance on Local Science and Technology Development Fund of Hebei Province","doi-asserted-by":"publisher","award":["254Z1904G"],"award-info":[{"award-number":["254Z1904G"]}],"id":[{"id":"10.13039\/501100008238","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003482","name":"Hebei Education Department","doi-asserted-by":"publisher","award":["CXY2025041"],"award-info":[{"award-number":["CXY2025041"]}],"id":[{"id":"10.13039\/501100003482","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008774","name":"Shijiazhuang University","doi-asserted-by":"crossref","award":["241791157A"],"award-info":[{"award-number":["241791157A"]}],"id":[{"id":"10.13039\/501100008774","id-type":"DOI","asserted-by":"crossref"}]},{"award":["241791157A"],"award-info":[{"award-number":["241791157A"]}],"id":[{"id":"https:\/\/ror.org\/028rmam09","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the challenges in personnel detection in underground coal mines, such as uneven lighting, dust occlusion, cluttered backgrounds, and significant scale variation of targets, this paper proposes a lightweight object detection method named YOLO-SCC. Based on the lightweight detection network YOLO26n, the SCC lightweight enhancement structure, composed of SPDConv, CBAM, and ContextAggregation, is constructed to improve feature representation and target recognition capability in complex underground environments while striving to maintain model compactness. Specifically, SPDConv optimizes the downsampling process to reduce the loss of feature details, which helps preserve edge and texture information of miners under low-light conditions, at long distances, or at small scales. CBAM adaptively weights features from both channel and spatial dimensions, enhancing the model\u2019s focus on the main body of miners and suppressing irrelevant interference from light reflections, equipment structures, and dust noise. The ContextAggregation module aggregates richer contextual semantic information, strengthening the model\u2019s discriminative ability for personnel targets under occlusion, dense distribution, and complex backgrounds. Experimental results show that with only a modest increase in parameters (from 2.38 MB to 2.88 MB) and computational cost (from 5.2 GFLOPs to 6.0 GFLOPs), YOLO-SCC achieves precision of 88.8%, recall of 82.2%, mAP50 of 85.3%, and mAP50-95 of 51.2%. These represent improvements of 6.1, 0.9, 0.1, and 3.3 percentage points, respectively, over the baseline YOLO26n. The results suggest that YOLO-SCC primarily improves false-positive suppression and localization quality in complex underground environments while maintaining a lightweight model scale.<\/jats:p>","DOI":"10.3390\/s26154897","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T14:16:27Z","timestamp":1785766587000},"page":"4897","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLO-SCC: A Lightweight Object Detection Method for Mine Safety"],"prefix":"10.3390","volume":"26","author":[{"given":"Zhe","family":"Wu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3984-8615","authenticated-orcid":false,"given":"Xuanrui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanping","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"Shijiazhuang Coal Mining Machinery Co., Ltd., Shijiazhuang 050031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingna","family":"Li","sequence":"additional","affiliation":[{"name":"Shijiazhuang Coal Mining Machinery Co., Ltd., Shijiazhuang 050031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115514","DOI":"10.1016\/j.measurement.2024.115514","article-title":"Vision-Based Obstacle Detection in Dangerous Region of Coal Mine Driverless Rail Electric Locomotives","volume":"239","author":"Yang","year":"2025","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115516","DOI":"10.1016\/j.measurement.2024.115516","article-title":"Development of Productivity Model of Continuous Miner Operators Working in Hazardous Underground Mine Environmental Conditions","volume":"239","author":"Roy","year":"2025","journal-title":"Measurement"},{"key":"ref_3","first-page":"1","article-title":"Design of Intelligent Early Warning System for Fully Enclosed Transportation of Monorail Crane in Coal Mine","volume":"56","author":"Wang","year":"2025","journal-title":"Eng. 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