{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:58:31Z","timestamp":1774425511479,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52275508"],"award-info":[{"award-number":["52275508"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Steel surface defects significantly impact product quality and safety in industrial settings. Traditional defect detection methods suffer from inefficiencies and limitations. This study introduces an innovative deep learning architecture, CTG-YOLO, designed to enhance multi-scale defect detection accuracy on steel surfaces. By integrating a CBY parallel network structure, a TFF-PANet neck network, and a GS-Head detection head, our model achieves superior feature extraction and fusion capabilities. Experimental results on the NEU-DET and GC10-DET datasets demonstrate significant improvements, with mean Average Precision (mAP) scores of 76.55% and 69.94%, respectively, outperforming the original YOLOv8s by 3.72% and 3.14%. This research provides a robust foundation for industrial defect detection applications.<\/jats:p>","DOI":"10.3390\/s26062001","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T13:07:06Z","timestamp":1774271226000},"page":"2001","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Multi-Scale Defect Detection in Steel Surfaces via Innovative Deep Learning Architecture"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1910-520X","authenticated-orcid":false,"given":"Zhaoxuan","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2959-5533","authenticated-orcid":false,"given":"Yan","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1007\/s00371-024-03442-y","article-title":"Adversarial-based refinement dual-branch network for semi-supervised salient object detection of strip steel surface defects","volume":"41","author":"Sun","year":"2024","journal-title":"Vis. 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