End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
This work addresses damage assessment for infrastructure safety, but it is incremental as it builds on existing deep learning frameworks.
The paper tackles automated crack detection in structures after extreme events by proposing enhanced Mask R-CNN methods with PANet and HRNet, achieving significant improvements over alternative networks on three public datasets.
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.