Research on the crack detection method of black coating based on machine vision and deep learning

作者：Yunlong Jia, Da Mu, Fang Liu, Zheng Guo, Xiao Qin · 发表于：Optics Express · 年份：2025 · DOI：10.1364/oe.568123 · 被引用次数：2 · 研究领域：Infrastructure Maintenance and Monitoring、Industrial Vision Systems and Defect Detection、Non-Destructive Testing Techniques

In practical applications of the black high-radiation coating on the surface of porous materials, thermal stress can lead to the formation of micro-cracks on the surface, which may compromise the overall structural integrity and safety. This study proposes a machine vision sampling system to address the challenge of low-contrast imaging of small cracks in black coatings, affecting real-time detection accuracy. The system investigates the effects of various lighting methods on crack-background contrast. Additionally, it performs data augmentation and annotation on collected images to construct a dataset for black coating crack target detection. A BCC-YOLO crack detection algorithm is introduced, which builds upon the YOLOv10s model by incorporating an ADown module to replace traditional Conv and SCDown down-sampling modules, reducing both the number of model parameters and computational complexity while enhancing the model's feature extraction capability for small cracks. Furthermore, an iEMA attention mechanism module is integrated into the small-target detection layer, which combines the iRMB module with the EMA attention mechanism. This fusion maintains effective attention while reducing the number of parameters. The UIoU loss function replaces CIoU to accelerate convergence and improve training stability. Experimental results demonstrate that, compared to YOLOv10s, BCC-YOLO achieves improvements of 9.7%, 11.2%, 10.8%, and 9.8% in precision (P), recall (R), mAP 50 , and mAP...