论文标题
在各种规模的极端事件中的自动损害检测的端到端深度学习方法
End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
论文作者
论文摘要
提出了强大的面膜R-CNN（掩模区域循环神经网络）方法并测试以自动检测结构或其组件的裂纹或在极端事件（例如地球Quakes）中可能损坏的组件。我们策划了一个新的数据集，该数据集使用2,021个标记的图像进行培训和验证，并旨在找到端到端的深神经网络，以便在该领域进行破裂检测。随着数据增强和参数微调，将带有空间注意机制和高分辨率网络（HRNET）的路径聚集网络（Panet）引入了掩码R-CNN中。在三个具有低分辨率图像或高分辨率图像的公共数据集上的测试表明，所提出的方法可以对替代网络实现很大的改进，因此所提出的方法可能足以在实际应用中的各种量表中进行裂纹检测。
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.
