Robust Mask R-CNN (Mask Regional Convolu-tional 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 earth-quakes. 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.
@article{arxiv.2011.03098,
title = {End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales},
author = {Yongsheng Bai and Halil Sezen and Alper Yilmaz},
journal= {arXiv preprint arXiv:2011.03098},
year = {2020}
}