{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T20:00:26Z","timestamp":1777060826419,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T00:00:00Z","timestamp":1741996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004735","name":"Hunan Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2024JJ7144"],"award-info":[{"award-number":["2024JJ7144"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Hunan Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2023JJ50196"],"award-info":[{"award-number":["2023JJ50196"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In the aluminum electrolysis production workshop, heavy-load overhead cranes equipped with multi-functional operation terminals are responsible for critical tasks such as anode replacement, shell breaking, slag removal, and material feeding. The real-time monitoring of these four types of operation terminals is of the utmost importance for ensuring production safety. High-resolution cameras are used to capture dynamic scenes of operation. However, the terminals undergo morphological changes and rotations in three-dimensional space according to task requirements during operations, lacking rotational invariance. This factor complicates the detection and recognition of multi-form targets in 3D environment. Additionally, operations like striking and material feeding generate significant dust, often visually obscuring the terminal targets. The challenge of real-time multi-form object detection in high-resolution images affected by smoke and dust environments demands detection and dehazing algorithms. To address these issues, we propose the YOLOv8n-Al-Dehazing method, which achieves the precise detection of multi-functional material handling terminals in aluminum electrolysis workshops. To overcome the heavy computational costs associated with processing high-resolution images by using YOLOv8n, our method refines YOLOv8n through component substitution and integrates real-time dehazing preprocessing for high-resolution images, thereby reducing the image processing time. We collected on-site data to construct a dataset for experimental validation. Compared with the YOLOv8n method, our method approach increases inference speed by 15.54%, achieving 120.4 frames per second, which meets the requirements for real-time detection on site. Furthermore, compared with state-of-the-art detection methods and variants of YOLO, YOLOv8n-Al-Dehazing demonstrates superior performance, attaining an accuracy rate of 91.0%.<\/jats:p>","DOI":"10.3390\/info16030229","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T06:36:23Z","timestamp":1742193383000},"page":"229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["YOLOv8n-Al-Dehazing: A Robust Multi-Functional Operation Terminals Detection for Large Crane in Metallurgical Complex Dust Environment"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4380-6840","authenticated-orcid":false,"given":"Yifeng","family":"Pan","sequence":"first","affiliation":[{"name":"College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghong","family":"Long","sequence":"additional","affiliation":[{"name":"College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yejing","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1109\/CVPR.2005.177","article-title":"Histograms of oriented gradients for human detection","volume":"Volume 1","author":"Dalal","year":"2005","journal-title":"Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905)"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object Detection with Discriminatively Trained Part-Based Models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. 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