  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.
