{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T07:51:05Z","timestamp":1780645865955,"version":"3.54.1"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Province Key Construction Discipline Research Ability Enhancement Project","award":["2024ZDJS071"],"award-info":[{"award-number":["2024ZDJS071"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>The magnetic flux leakage (MFL) method is widely acknowledged as a highly effective non-destructive evaluation (NDE) technique for detecting local damage in ferromagnetic structures such as steel wire ropes. In this study, a multi-channel MFL sensor module was developed, incorporating a purpose-designed Hall sensor array and magnetic yokes specifically shaped for steel cables. To validate the proposed damage detection method, artificial damages of varying degrees were inflicted on wire rope specimens through experimental testing. The MFL sensor module facilitated the scanning of the damaged specimens and measurement of the corresponding MFL signals. In order to improve the signal-to-noise ratio, a comprehensive set of signal processing steps, including channel equalization and normalization, was implemented. Subsequently, the detected MFL distribution surrounding wire rope defects was transformed into MFL images. These images were then analyzed and processed utilizing an object detection method, specifically employing the YOLOv9 network, which enables accurate identification and localization of defects. Furthermore, a quantitative defect detection method based on image size was introduced, which is effective for quantifying defects using the dimensions of the anchor frame. The experimental results demonstrated the effectiveness of the proposed approach in detecting and quantifying defects in steel cables, which combines deep learning-based analysis of MFL images with the non-destructive inspection of steel cables.<\/jats:p>","DOI":"10.3390\/jsan14040080","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T10:13:51Z","timestamp":1754475231000},"page":"80","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Nondestructive Inspection of Steel Cables Based on YOLOv9 with Magnetic Flux Leakage Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1696-3277","authenticated-orcid":false,"given":"Min","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan 523668, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Ding","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zehao","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6157-3457","authenticated-orcid":false,"given":"Bingchun","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan 523668, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan 523668, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuqin","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Advanced Engineering, Great Bay University, Dongguan 523000, China"},{"name":"School of Electronics and Information Engineering, Wuyi University, Jiangmen 529020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.1016\/j.matpr.2017.11.620","article-title":"Advances and Researches on Non Destructive Testing: A Review","volume":"5","author":"Dwivedi","year":"2018","journal-title":"Mater. 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