Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Smart Structures and Systems Volume 31, Number 4, April 2023 , pages 365-381 DOI: https://doi.org/10.12989/sss.2023.31.4.365 |
|
|
|
Deep learning-based post-disaster building inspection with channel-wise attention and semi-supervised learning |
||
Wen Tang, Tarutal Ghosh Mondal, Rih-Teng Wu, Abhishek Subedi and Mohammad R. Jahanshahi
|
||
| Abstract | ||
| The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2. | ||
| Key Words | ||
| building visual inspection; channel-wise attention; semantic segmentation; semi-supervised learning | ||
| Address | ||
| (1) Wen Tang, Abhishek Subedi, Mohammad R. Jahanshahi: Lyles School of Civil Engineering, Purdue University, West Lafayette, USA; (2) Tarutal Ghosh Mondal: Department of Civil, Architecture and Environment Engineering, Missouri University of Science and Technology, Rolla, USA; (3) Rih-Teng Wu: Department of Civil Engineering, National Taiwan University, Taipei, Taiwan; (4) Mohammad R. Jahanshahi: Elmore Family School of Electrical and Computer Engineering (Courtesy), Purdue University, West Lafayette, USA. | ||