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논문 기본 정보

자료유형
학술저널
저자정보
Ying Zhou (Tongji University) Shiqiao Meng (Tongji University) Lezhi Gu (Tongji University) Abouzar Jafari (Tongji University)
저널정보
국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal 제33권 제6호
발행연도
2024.6
수록면
449 - 463 (15page)

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초록· 키워드

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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.

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