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Title: End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
Robust Mask R-CNN (Mask Regional Convolutional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earthquakes. We curated a new dataset with 2,021 labeled images for training and validation and aimed to find end-to-end deep neural networks for crack detection in the field. With data augmentation and parameters fine-tuning, Path Aggregation Network (PANet) with spatial attention mechanisms and High- resolution Network (HRNet) are introduced into Mask R-CNNs. The tests on three public datasets with low- or high-resolution images demonstrate that the proposed methods can achieve a big improvement over alternative networks, so the proposed method may be sufficient for crack detection for a variety of scales in real applications.
Authors:
; ;
Award ID(s):
2036193
Publication Date:
NSF-PAR ID:
10352789
Journal Name:
25th International Conference on Pattern Recognition (ICPR)
Page Range or eLocation-ID:
6640 to 6647
Sponsoring Org:
National Science Foundation
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