DETECTING CRACKS AND SPALLING AUTOMATICALLY IN EXTREME EVENTS BY END-TO-END DEEP LEARNING FRAMEWORKS
DETECTING CRACKS AND SPALLING AUTOMATICALLY IN EXTREME EVENTS BY END-TO-END DEEP LEARNING FRAMEWORKS
复制标题
DOI：
10.5194/isprs-annals-v-2-2021-161-2021
复制
发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Y. Bai;H. Sezen;A. Yilmaz
中科院分区：
文献类型：
--
作者：
Y. Bai;H. Sezen;A. Yilmaz
抽象的。在本文中，我们开发并实现了端到端的深度学习方法，以自动检测建筑物和桥梁在大地震等极端事件中的两种重要类型的结构故障，即裂缝和剥落。总共对2，229张图像进行了注释，并用于训练和验证三个新开发的掩码区域卷积神经网络（Mask R-CNN）。此外，三组不同灾害的公共图像被用来测试这些模型的准确性。对于检测和标记这两种类型的结构故障，所提出的方法之一，可以达到67.6%和81.1%，分别从现场调查收集的低分辨率和高分辨率图像的准确性。结果表明，它是可行的，使用所提出的端到端的方法，自动定位和分割的损害，使用2D图像，可以帮助人类专家在灾害的情况下。
Abstract. In this paper, we develop and implement end-to-end deep learning approaches to automatically detect two important types of structural failures, cracks and spalling, of buildings and bridges in extreme events such as major earthquakes. A total of 2,229 images were annotated, and are used to train and validate three newly developed Mask Regional Convolutional Neural Networks (Mask R-CNNs). In addition, three sets of public images for different disasters were used to test the accuracy of these models. For detecting and marking these two types of structural failures, one of proposed methods can achieve an accuracy of 67.6% and 81.1%, respectively, on low- and high-resolution images collected from field investigations. The results demonstrate that it is feasible to use the proposed end-to-end method for automatically locating and segmenting the damage using 2D images which can help human experts in cases of disasters.