{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:33:20Z","timestamp":1760060000814,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Autonomous Region Key R&amp;D and Achievement Transformation Program Project","award":["2023YFSW0003","2024QNJS116"],"award-info":[{"award-number":["2023YFSW0003","2024QNJS116"]}]},{"name":"Basic Research Fund Project for Autonomous Region Universities","award":["2023YFSW0003","2024QNJS116"],"award-info":[{"award-number":["2023YFSW0003","2024QNJS116"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Performance degradation of wind turbine blades often stems from geometric asymmetry induced by damage. Existing methods for assessing damage face challenges in balancing accuracy and efficiency due to their limited ability to capture fine-grained geometric asymmetries associated with multi-scale damage under complex background interference. To address this, based on the high-speed detection model YOLOv10-N, this paper proposes a novel detection model named MMC-YOLO. First, the Multi-Scale Perception Gated Convolution (MSGConv) Module was designed, which constructs a full-scale receptive field through multi-branch fusion and channel rearrangement to enhance the extraction of geometric asymmetry features. Second, the Multi-Scale Enhanced Feature Pyramid Network (MSEFPN) was developed, integrating dynamic path aggregation and an SENetv2 attention mechanism to suppress background interference and amplify damage response. Finally, the Channel-Compensated Filtering (CCF) module was constructed to preserve critical channel information using a dynamic buffering mechanism. Evaluated on a dataset of 4818 wind turbine blade damage images, MMC-YOLO achieves an 82.4% mAP [0.5:0.95], representing a 4.4% improvement over the baseline YOLOv10-N model, and a 91.1% recall rate, an 8.7% increase, while maintaining a lightweight parameter count of 4.2 million. This framework significantly enhances geometric asymmetry defect detection accuracy while ensuring real-time performance, meeting engineering requirements for high efficiency and precision.<\/jats:p>","DOI":"10.3390\/sym17081183","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T10:28:09Z","timestamp":1753352889000},"page":"1183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MMC-YOLO: A Lightweight Model for Real-Time Detection of Geometric Symmetry-Breaking Defects in Wind Turbine Blades"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3499-1535","authenticated-orcid":false,"given":"Caiye","family":"Liu","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China"},{"name":"Inner Mongolia Autonomous Region Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems, Baotou 014010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China"},{"name":"Inner Mongolia Autonomous Region Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems, Baotou 014010, China"},{"name":"School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7375-504X","authenticated-orcid":false,"given":"Xinyu","family":"Ge","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China"},{"name":"Inner Mongolia Autonomous Region Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems, Baotou 014010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xunmeng","family":"An","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China"},{"name":"Inner Mongolia Autonomous Region Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems, Baotou 014010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5103-7587","authenticated-orcid":false,"given":"Nan","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China"},{"name":"Inner Mongolia Autonomous Region Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems, Baotou 014010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ou, K., Gao, S., Wang, Y., Zhai, B., and Zhang, W. 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