{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:53:29Z","timestamp":1787316809768,"version":"build-2736575974"},"reference-count":162,"publisher":"Association for Computing Machinery (ACM)","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"abstract":"<jats:p>Material defects (MD) represent a primary challenge affecting product performance and giving rise to safety issues in related products. The rapid and accurate identification and localization of MD constitute crucial research endeavors in addressing contemporary challenges associated with MD. In recent years, propelled by the swift advancement of machine learning (ML) technologies, particularly exemplified by deep learning, ML has swiftly emerged as the core technology and a prominent research direction for material defect detection (MDD). Through a comprehensive review of the latest literature, we systematically survey the ML techniques applied in MDD into five categories: unsupervised learning, supervised learning, semi-supervised learning, reinforcement learning, and generative learning. We provide a detailed analysis of the main principles and techniques used, together with the advantages and potential challenges associated with these techniques. Furthermore, the survey focuses on the techniques for defect detection in composite materials, which are important types of materials enjoying increasingly wide application in various industries such as aerospace, automotive, construction, and renewable energy. Finally, the survey explores potential future directions in MDD utilizing ML technologies. This survey consolidates ML-based MDD literature and provides a foundation for future research and practice.<\/jats:p>","DOI":"10.1145\/3730576","type":"journal-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T07:08:51Z","timestamp":1745305731000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["A Comprehensive Survey on Machine Learning Driven Material Defect Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3349-5161","authenticated-orcid":false,"given":"Jun","family":"Bai","sequence":"first","affiliation":[{"name":"Centre for Future Materials, University of Southern Queensland, Toowoomba, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4753-8161","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4812-0285","authenticated-orcid":false,"given":"Tristan","family":"Shelley","sequence":"additional","affiliation":[{"name":"Centre for Future Materials, University of Southern Queensland, Toowoomba, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1610-0922","authenticated-orcid":false,"given":"Peter","family":"Schubel","sequence":"additional","affiliation":[{"name":"Centre for Future Materials, University of Southern Queensland, Toowoomba, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2903-5708","authenticated-orcid":false,"given":"David","family":"Twine","sequence":"additional","affiliation":[{"name":"Centre for Future Materials, University of Southern Queensland, Toowoomba, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5851-2695","authenticated-orcid":false,"given":"John","family":"Russell","sequence":"additional","affiliation":[{"name":"Air Force Research Laboratory,  Wright-Patterson AFB, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7833-2986","authenticated-orcid":false,"given":"Xuesen","family":"Zeng","sequence":"additional","affiliation":[{"name":"Centre for Future Materials, University of Southern Queensland, Toowoomba, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7167-6970","authenticated-orcid":false,"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","first-page":"4203","DOI":"10.1109\/TIA.2022.3151560","article-title":"Automated surface defect detection in metals: A comparative review of object detection and semantic segmentation using deep learning","volume":"58","author":"Usamentiaga Rub\u00e9n","year":"2022","unstructured":"Rub\u00e9n Usamentiaga, Dario\u00a0G Lema, Oscar\u00a0D Pedrayes, and Daniel\u00a0F Garcia. 2022. 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