{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:38:21Z","timestamp":1780447101723,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Provincial Natural Science Foundation","award":["LH2022F038"],"award-info":[{"award-number":["LH2022F038"]}]},{"name":"Provincial Natural Science Foundation","award":["XPPY202208"],"award-info":[{"award-number":["XPPY202208"]}]},{"name":"Cultivation Project of National Natural Science Foundation of Harbin Normal University","award":["LH2022F038"],"award-info":[{"award-number":["LH2022F038"]}]},{"name":"Cultivation Project of National Natural Science Foundation of Harbin Normal University","award":["XPPY202208"],"award-info":[{"award-number":["XPPY202208"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Strip steel surface defect detection has become a crucial step in ensuring the quality of strip steel production. To address the issues of low detection accuracy and long detection times in strip steel surface defect detection algorithms caused by varying defect sizes and blurred images during acquisition, this paper proposes a lightweight strip steel surface defect detection network, YOLO-SDS, based on an improved YOLOv8. Firstly, StarNet is utilized to replace the backbone network of YOLOv8, achieving lightweight optimization while maintaining accuracy. Secondly, a lightweight module DWR is introduced into the neck and combined with the C2f feature extraction module to enhance the model\u2019s multi-scale feature extraction capability. Finally, an occlusion-aware attention mechanism SEAM is incorporated into the detection head, enabling the model to better capture and process features of occluded objects, thus improving performance in complex scenarios. Experimental results on the open-source NEU-DET dataset show that the improved model reduces parameters by 34.4% compared with the original YOLOv8 algorithm while increasing average detection accuracy by 1.5%. And it shows good generalization performance on the deepPCB dataset. Compared with other defect detection models, YOLO-SDS offers significant advantages in terms of parameter count and detection speed. Additionally, ablation experiments validate the effectiveness of each module.<\/jats:p>","DOI":"10.3390\/s24196495","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T12:22:48Z","timestamp":1728476568000},"page":"6495","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Lightweight Strip Steel Surface Defect Detection Network Based on Improved YOLOv8"],"prefix":"10.3390","volume":"24","author":[{"given":"Yuqun","family":"Chu","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic Engineering, Harbin Normal University, Harbin 150025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6779-4111","authenticated-orcid":false,"given":"Xianwei","family":"Rong","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic Engineering, Harbin Normal University, Harbin 150025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109454","DOI":"10.1016\/j.measurement.2021.109454","article-title":"Development of a YOLO-V3-Based Model for Detecting Defects on Steel Strip Surface","volume":"182","author":"Kou","year":"2021","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TPAMI.1983.4767445","article-title":"A Real-Time Automated Visual Inspection System for Hot Steel Slabs","volume":"PAMI-5","author":"Suresh","year":"1983","journal-title":"IEEE Trans. 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