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Smart Structures and Systems Volume 33, Number 6, June 2024 , pages 449-463 DOI: https://doi.org/10.12989/sss.2024.33.6.449 |
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A label-free high precision automated crack detection method based on unsupervised generative attentional networks and swin-crackformer |
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Shiqiao Meng, Lezhi Gu, Ying Zhou and Abouzar Jafari
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| Abstract | ||
| Automated crack detection is crucial for structural health monitoring and post-earthquake rapid damage detection. However, realizing high precision automatic crack detection in the absence of corresponding manual labeling presents a formidable challenge. This paper presents a novel crack segmentation transfer learning method and a novel crack segmentation model called Swin-CrackFormer. The proposed method facilitates efficient crack image style transfer through a meticulously designed data preprocessing technique, followed by the utilization of a GAN model for image style transfer. Moreover, the proposed Swin-CrackFormer combines the advantages of Transformer and convolution operations to achieve effective local and global feature extraction. To verify the effectiveness of the proposed method, this study validates the proposed method on three unlabeled crack datasets and evaluates the Swin-CrackFormer model on the METU dataset. Experimental results demonstrate that the crack transfer learning method significantly improves the crack segmentation performance on unlabeled crack datasets. Moreover, the Swin-CrackFormer model achieved the best detection result on the METU dataset, surpassing existing crack segmentation models. | ||
| Key Words | ||
| crack detection; deep learning; unsupervised generative attentional networks; vision Transformer | ||
| Address | ||
| State Key Laboratory of Disaster Reduction in Civil Engineering, College of Civil Engineering, Tongji University, 1239 Siping Rd., Shanghai, 200092, China. | ||