- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | LI Zewei, YANG Yongqing, LIAO Man, XIE Mingzhi, LIU Yu, HUANG Shengqian. Structural Crack Detection Based on Computer Vision and Hybrid Measurement Technology[J]. Journal of Southwest Jiaotong University, 2025, 60(6): 1455-1464. doi: 10.3969/j.issn.0258-2724.20230700
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Concrete surface crack detection provides essential technical data and decision-making elements for the operation and maintenance of bridge structures. Crack identification is a key step in structural crack detection. However, the integration between crack target identification and information extraction is low. To this end, a new method for identifying structural cracks by using computer vision and hybrid measurement technology was proposed. Firstly, the You Only Look Once version 8 (YOLOv8) target recognition algorithm was employed to achieve rapid identification and localization of structural cracks. A super-resolution U-net (SR-UNet) crack segmentation model was developed based on the dense deep back-projection network (D-DBPN) and UNet, and boundary loss was introduced to improve the previous loss function, which addressed the imbalance between positive and negative samples and enabled precise pixel-level crack extraction. By using morphological techniques such as connected domain denoising and edge detection and a hybrid method of the shortest distance and orthogonal skeleton, the crack width at the pixel level was measured. A dataset of recognition and localization containing 3 123 crack images was created by using LabelImg software for model training and testing. The research results indicate that the YOLOv8 model achieves an accuracy of 83.41%, a recall rate of 84.93%, and an
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