End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales

• URL: http://arxiv.org/abs/2011.03098v1
• Date: Thu, 5 Nov 2020 21:21:19 GMT
• Title: End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
• Authors: Yongsheng Bai, Halil Sezen, Alper Yilmaz
• Abstract summary: Mask R-CNN is 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.
• Score: 0.4297070083645048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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