{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T21:53:06Z","timestamp":1777499586220,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Coal remains a vital energy resource and plays a key role in national development. Ensuring the safety of underground mining personnel is essential, and intelligent algorithms are increasingly used to detect miners in surveillance footage. However, complex underground environments\u2014characterised by poor lighting, occlusions, irregular postures, and reflective gear\u2014make accurate detection difficult. This study proposes improvements to the YOLOv10-N object detection model for miner detection. Using 37,463 annotated images from real mining environments, we propose three main enhancements: a Coordinate Attention (CA) mechanism to highlight important spatial features, a Dynamic Head (DyHead) module to improve multi-scale feature fusion, and the Efficient IoU (EIOU) loss function to enhance bounding box regression and speed up convergence. While CA, DyHead, and EIOU are established methods, their synergistic integration for asymmetric miner detection (e.g., occluded limbs, uneven lighting) presents a novel application-specific optimisation. Experimental results confirm that the enhanced model significantly outperforms the original. It achieves 92.69% accuracy, 87.53% recall, and an average accuracy of 89.9%, with a practical detection effect of 68.24%. These findings show that the proposed method improves both accuracy and robustness in challenging mining conditions while maintaining processing efficiency.<\/jats:p>","DOI":"10.3390\/sym17091435","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T08:04:15Z","timestamp":1756886655000},"page":"1435","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Asymmetric Object Recognition Process for Miners\u2019 Safety Based on Improved YOLOv10 Technology"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2651-899X","authenticated-orcid":false,"given":"Diana","family":"Novak","sequence":"first","affiliation":[{"name":"Institute of General Engineering, Empress Catherine II Saint Petersburg Mining University, 2, 21st Line, St. Petersburg 199106, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1822-7117","authenticated-orcid":false,"given":"Yuriy","family":"Kozhubaev","sequence":"additional","affiliation":[{"name":"Faculty of Energy, Empress Catherine II Saint Petersburg Mining University, 2, 21st Line, St. Petersburg 199106, Russia"}]},{"given":"Vyacheslav","family":"Potekhin","sequence":"additional","affiliation":[{"name":"Higher School of Cyberphysical Systems & Control, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}]},{"given":"Haodong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Higher School of Cyberphysical Systems & Control, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}]},{"given":"Roman","family":"Ershov","sequence":"additional","affiliation":[{"name":"JSC \u201cVorkutaugol\u201d, Vorkuta 169908, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.25018\/0236_1493_2022_102_0_185","article-title":"Zoning pipeline routes according to the degree of danger of accidents using geoinformation systems and artificial neural networks","volume":"2","author":"Kiselev","year":"2022","journal-title":"Min. 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