ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 411–417, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-411-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 411–417, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-411-2020

  03 Aug 2020

03 Aug 2020

DEEP CASCADED NEURAL NETWORKS FOR AUTOMATIC DETECTION OF STRUCTURAL DAMAGE AND CRACKS FROM IMAGES

Y. Bai, B. Zha, H. Sezen, and A. Yilmaz Y. Bai et al.
  • Dept. of Civil, Environmental and Geodetic Engineering, The Ohio State University, 2070 Neil Avenue, Columbus, Ohio, USA

Keywords: deep learning, structural damage detection, cracking detection, cracking localization, ResNet, U-Net

Abstract. In this paper, two different convolutional neural networks (CNNs) are applied on images for automated structural damage detection (SDD) in earthquake damaged structures and cracking localization (e.g., detection of cracks, their widths and distributions) at various scales, such as pixel level, object level, and structural level. The proposed method has two main steps: 1) diagnosis, and 2) localization of cracking or other damage. At first a residual CNN with transfer learning is employed to classify the damage in the structures and structural components. This step performs damage detection using two public datasets. The second step uses another CNN with U-Net structure to locate the cracking on low resolution images. The implementations using public and self-collected datasets show promising performance for a problem that had remained a challenge in the structure engineering field for a long time and indicate that the proposed approach can perform detection and localization of structural damage with an acceptable accuracy.