Crack Recognition and Quantitative Analysis Based on Deep Learning

FAN Cunjun; JIN Songyan; JIN Nan; SHI Zhongqi; WU Yongjingbang; HAO Xintian

doi:10.3724/j.gyjzG24061802

Volume 54 Issue 8

Aug. 2024

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FAN Cunjun, JIN Songyan, JIN Nan, SHI Zhongqi, WU Yongjingbang, HAO Xintian. Crack Recognition and Quantitative Analysis Based on Deep Learning[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(8): 126-132. doi: 10.3724/j.gyjzG24061802

Citation:

FAN Cunjun, JIN Songyan, JIN Nan, SHI Zhongqi, WU Yongjingbang, HAO Xintian. Crack Recognition and Quantitative Analysis Based on Deep Learning[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(8): 126-132. doi: 10.3724/j.gyjzG24061802

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Crack Recognition and Quantitative Analysis Based on Deep Learning

doi: 10.3724/j.gyjzG24061802

FAN Cunjun^1,2,3,
JIN Songyan^{1,2,3
,
,},
JIN Nan^1,2,3,
SHI Zhongqi^1,2,3,
WU Yongjingbang^1,2,3,
HAO Xintian^1,2,3

1. Shenzhen Urban Public Safety and Technology Institute, Shenzhen Key Laboratory of Urban Disasters Digital Twin, Shenzhen 518023, China;
2. Safe Urban Development Institute of Science and Technology (Shenzhen), Shenzhen 518023, China;
3. Key Laboratory for Urban Safety Risk Monitoring and Early Warning of Emergency Management Department, Shenzhen 518023, China

Received Date: 2024-06-18
Available Online: 2024-09-19

Abstract

Abstract

Cracks are a common form of surface damage in concrete structures and have significant implications for assessing structural performance. The use of computer vision techniques for crack recognition and quantification on the surface of concrete structures has been widely studied. However, deep learning-based crack recognition techniques rely on large-scale crack datasets for training. To address this issue, the paper proposed a data augmentation method based on style transfer networks. A large-scale, complex-background crack dataset was constructed by using a small amount of crack data and various background image data. The YoloV8 network model was trained to achieve crack recognition and segmentation. Based on the crack characteristics, isolated and tiny areas in the recognition results were filtered. Based on this, crack width quantification analysis was performed based on known reference markers, and the experimental results showed that the calculation error of crack widths was basically controlled within 20%.
- deep learning,
- data augmentation,
- crack recognition,
- crack width

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References(19)

References

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