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

Zhihang Li, Mengqi Huang, Pengxuan Ji, Huamei Zhu, Qianbing Zhang

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)153-166
Number of pages14
JournalSmart Structures and Systems
Issue number1
Publication statusPublished - Jan 2022


  • CNN
  • Crack detection
  • Data imbalance
  • Feature extraction
  • Loss function

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