{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:16:13Z","timestamp":1760058973942,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"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>Detecting aluminum defects in industrial environments presents significant challenges related to low-resolution images, subtle damage features, and an imbalance between easy and difficult samples. The You Only Look Once\u2013Aluminum (YOLO-AL) algorithm proposed in this paper addresses these challenges. Firstly, to enhance the model\u2019s performance on low-resolution images and small object detection, as well as to improve its flexibility and adaptability, C2f-US replaces the first two CSP bottleneck with 2 Convolutions (C2f) layers in the original Backbone network. Secondly, to boost multi-scale context capture and strip defect detection, a CPMSCA mechanism with a class-symmetric structure is proposed and integrated at the end of the Backbone network. Thirdly, to efficiently capture both high-level semantics and low-level spatial details, and improve detection of complex aluminum surface defects, ODE-RepGFPN is introduced to replace the entire Neck network. Finally, to address the imbalance between hard and easy samples, Focaler-WIoU is proposed. Extensive experiments conducted on the publicly available AliCloud dataset (APDDD) demonstrate that YOLO-AL achieves 86.5%, 77.8%, and 81.5% for Precision, Recall, and mAP@0.5, respectively, surpassing both the baseline model and other state-of-the-art methods. The model can be integrated with an industrial camera system for the automated inspection of aluminum profiles in a production environment.<\/jats:p>","DOI":"10.3390\/sym17050724","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T06:18:51Z","timestamp":1746771531000},"page":"724","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["You Only Look Once\u2013Aluminum: A Detection Model for Complex Aluminum Surface Defects Based on Improved YOLOv8"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2934-038X","authenticated-orcid":false,"given":"Jiashu","family":"Han","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213212, China"}]},{"given":"Huiye","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213212, China"}]},{"given":"Yitong","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213212, China"}]},{"given":"Shudong","family":"Zhuang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213212, China"}]},{"given":"Chengyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213212, China"}]},{"given":"Hua","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213212, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"52","DOI":"10.18485\/aeletters.2018.3.2.2","article-title":"Application of Aluminum and Aluminum Alloys in Engineering","volume":"3","author":"Stojanovic","year":"2018","journal-title":"Appl. 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