Smart Structures and Systems

  • 제29권1호
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  • Pages.153-166
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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

One-step deep learning-based method for pixel-level detection of fine cracks in steel girder images

  • Li, Zhihang (Department of Civil Engineering, Monash University) ;
  • Huang, Mengqi (Department of Civil Engineering, Monash University) ;
  • Ji, Pengxuan (Department of Civil Engineering, Monash University) ;
  • Zhu, Huamei (Department of Civil Engineering, Monash University) ;
  • Zhang, Qianbing (Department of Civil Engineering, Monash University)
  • 투고 : 2021.04.15
  • 심사 : 2021.08.07
  • 발행 : 2022.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

Identifying fine cracks in steel bridge facilities is a challenging task of structural health monitoring (SHM). This study proposed an end-to-end crack image segmentation framework based on a one-step Convolutional Neural Network (CNN) for pixel-level object recognition with high accuracy. To particularly address the challenges arising from small object detection in complex background, efforts were made in loss function selection aiming at sample imbalance and module modification in order to improve the generalization ability on complicated images. Specifically, loss functions were compared among alternatives including the Binary Cross Entropy (BCE), Focal, Tversky and Dice loss, with the last three specialized for biased sample distribution. Structural modifications with dilated convolution, Spatial Pyramid Pooling (SPP) and Feature Pyramid Network (FPN) were also performed to form a new backbone termed CrackDet. Models of various loss functions and feature extraction modules were trained on crack images and tested on full-scale images collected on steel box girders. The CNN model incorporated the classic U-Net as its backbone, and Dice loss as its loss function achieved the highest mean Intersection-over-Union (mIoU) of 0.7571 on full-scale pictures. In contrast, the best performance on cropped crack images was achieved by integrating CrackDet with Dice loss at a mIoU of 0.7670.

키워드

과제정보

This study was supported by Monash University for the scholarships and the high-performance computation platform. The authors appreciate the organizations of the IPC-SHM 2020 ANCRiSST, Harbin Institute of Technology (China), and University of Illinois at Urbana-Champaign (USA) for their generously providing invaluable data from actual structures. The authors also would like to thank the chairs of IPC-SHM 2020 Prof. Hui Li, and Prof. Billie F. Spencer Jr for their leadership on the competition.

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