Smart Structures and Systems

  • 제29권1호
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  • Pages.221-235
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  • 2022
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Crack segmentation in high-resolution images using cascaded deep convolutional neural networks and Bayesian data fusion

  • Tang, Wen (Lyles School of Civil Engineering, Purdue University) ;
  • Wu, Rih-Teng (Department of Civil Engineering, National Taiwan University) ;
  • Jahanshahi, Mohammad R. (Lyles School of Civil Engineering, Purdue University)
  • 투고 : 2021.05.08
  • 심사 : 2021.09.09
  • 발행 : 2022.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.

키워드

과제정보

The authors would like to thank the organization of the IPC-SHM 2020: Harbin Institute of Technology, University of Illinois at Urbana-Champaign and ANCRiSST for providing the valuable data used in this study.

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