{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T05:20:54Z","timestamp":1783056054192,"version":"3.54.6"},"reference-count":21,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2024YFB4710500"],"award-info":[{"award-number":["2024YFB4710500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Aero-engine blades operate under extreme conditions involving high temperature, pressure, rotational speed, and cyclic loads, making them susceptible to surface defects such as micro-cracks. Due to their small scale, weak edges, low contrast, and elongated morphology, micro-cracks are easily affected by metallic reflections, uneven illumination, and complex background textures in borescope images, resulting in high missed-detection rates for conventional detection methods. To address these challenges, this study proposes an improved YOLO11-based framework for aero-engine blade micro-crack detection. The proposed method introduces P1\/P2 shallow high-resolution detection branches to enhance the perception of fine crack edges and textures, incorporates Focal Loss to alleviate foreground\u2013background imbalance, applies object-level Tversky Loss to strengthen false-negative constraints, and adopts a hard mining strategy to improve learning for difficult crack samples. Experiments conducted on a real aero-engine borescope image dataset demonstrate that the proposed model achieves a Precision of 0.9981, Recall of 0.9606, F1-score of 0.9790, mAP50 of 0.9781, and mAP50-95 of 0.6938 on an independent test set. Compared with the YOLO11 baseline, the proposed method significantly improves crack detection accuracy, localization quality, and robustness in complex borescope inspection scenarios.<\/jats:p>","DOI":"10.3390\/jimaging12070294","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T16:50:33Z","timestamp":1782924633000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Collaborative Optimization of High-Resolution Representation and Miss-Sensitive Supervision for Aero-Engine Micro-Crack Detection"],"prefix":"10.3390","volume":"12","author":[{"given":"Zixuan","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6944-8548","authenticated-orcid":false,"given":"Hongwei","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoming","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Bai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1621-2246","authenticated-orcid":false,"given":"Yongliang","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4107-8093","authenticated-orcid":false,"given":"Long","family":"Cui","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"53048","DOI":"10.1109\/ACCESS.2023.3280992","article-title":"Aero-Engine Blade Defect Detection: A Systematic Review of Deep Learning Models","volume":"11","author":"Abdulrahman","year":"2023","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107473","DOI":"10.1016\/j.ast.2022.107473","article-title":"Deep Learning-Based Borescope Image Processing for Aero-Engine Blade In-Situ Damage Detection","volume":"123","author":"Shang","year":"2022","journal-title":"Aerosp. 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