{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:00:43Z","timestamp":1773770443419,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Key R & D Program Project \u201cIntelligent Patrol and Defense System for Robots with Autonomous Control and Group Collaboration\u201d","award":["AB24010366"],"award-info":[{"award-number":["AB24010366"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Micro-crack detection on concrete surfaces is challenging because labeled micro-crack data are scarce, crack cues are extremely weak (often only a few pixels wide), and complex backgrounds (e.g., non-uniform illumination, shadows, and stains) degrade feature extraction; this study aims to improve both data availability and detection robustness for practical inspection. A Poisson image editing-based synthesis strategy is developed to generate visually coherent micro-crack samples via gradient-domain blending, and a Complex-Scene-Tolerant YOLO (CST-YOLO) detector is proposed on top of YOLOv10, following an \u201clighting decoupling\u2013global perception\u2013micro-feature enhancement\u201d design. CST-YOLO integrates an Lighting-Adaptive Preprocessing Module (LAPM) to suppress illumination\/shadow perturbations, a Spatial\u2013Channel Sparse Transformer (SCS-Former) to model long-range crack topology efficiently, and a Small Object Focus Block (SOFB) to enhance micro-scale cues under cluttered backgrounds. Experiments are conducted on a 650-image dataset (200 real and 450 synthesized), in which synthesized samples are used only for training, and the validation\/test sets contain only real images, with a 7:2:1 split. CST-YOLO achieves 0.990 mAP@0.5 and 0.926 mAP@0.5:0.95 at 139 FPS, and ablation results indicate complementary contributions from LAPM, SCS-Former, and SOFB. These results support the effectiveness of combining realistic synthesis and architecture-level robustness for real-time micro-crack detection in complex scenes.<\/jats:p>","DOI":"10.3390\/s26061883","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T14:50:48Z","timestamp":1773759048000},"page":"1883","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Synthetic Data Methods and Detection Models for Micro-Cracks"],"prefix":"10.3390","volume":"26","author":[{"given":"Yaotong","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianmiao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanhe","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Physical Science and Technology, Beijing University of Posts and Telecommunications, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhong","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"116908","DOI":"10.1016\/j.measurement.2025.116908","article-title":"Deep Learning for Surface Crack Detection in Civil Engineering: A Comprehensive Review","volume":"248","author":"Zhuang","year":"2025","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105328","DOI":"10.1016\/j.autcon.2024.105328","article-title":"Deep Learning-Based Structural Health Monitoring","volume":"161","author":"Cha","year":"2024","journal-title":"Autom. 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