{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T16:35:10Z","timestamp":1789835710480,"version":"4.0.1"},"reference-count":48,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"S&T Program of Hebei","award":["22282203Z"],"award-info":[{"award-number":["22282203Z"]}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["E2022209086"],"award-info":[{"award-number":["E2022209086"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface defects are inevitable in the production of steel. However, traditional methods in industrial production face great challenges in detecting complex defects. Therefore, we propose LCED-YOLO based on YOLOv11 for steel defect detection. Firstly, an edge information enhancement module, C3K2-MSE, is designed to strengthen the extraction of edge information. Secondly, LDConv is introduced to lightweight the neck structure and reduce parameters. Then, a lightweight decoupling head designed for model detection tasks is proposed, further achieving model lightweighting. Finally, by introducing a learnable attention factor to optimize the CIoU loss, we focused on locating difficult samples, enhancing the detection capability. A large number of experiments were conducted on the NEU-DET and GC10-DET datasets. Compared to YOLOv11, the mAP50 of the proposed model improved by 2.6% and 3.3%, attaining 79.8% and 70.3%, respectively. It decreased 19% of parameters and 23% of floating-point operations, fulfilling the needs of lightweight and detection precision.<\/jats:p>","DOI":"10.3390\/s25247527","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T12:57:41Z","timestamp":1765457861000},"page":"7527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Efficient Lightweight Method for Steel Surface Defect Detection"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1536-5486","authenticated-orcid":false,"given":"Aiyun","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weimin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109316","DOI":"10.1016\/j.measurement.2021.109316","article-title":"Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN","volume":"178","author":"Xu","year":"2021","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5004914","DOI":"10.1109\/TIM.2021.3132082","article-title":"Dense Attention-Guided Cascaded Network for Salient Object Detection of Strip Steel Surface Defects","volume":"71","author":"Zhou","year":"2022","journal-title":"IEEE Trans. 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