DEEP CASCADED NEURAL NETWORKS FOR AUTOMATIC DETECTION OF STRUCTURAL DAMAGE AND CRACKS FROM IMAGES
DEEP CASCADED NEURAL NETWORKS FOR AUTOMATIC DETECTION OF STRUCTURAL DAMAGE AND CRACKS FROM IMAGES
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DOI：
10.5194/isprs-annals-v-2-2020-411-2020
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Y. Bai;B. Zha;H. Sezen;A. Yilmaz
中科院分区：
文献类型：
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作者：
Y. Bai;B. Zha;H. Sezen;A. Yilmaz
抽象。在本文中，两种不同的卷积神经网络（CNN）被应用于图像上，用于地震受损结构中的自动结构损伤检测（SDD）和裂缝定位（例如，裂缝、其宽度和分布的检测）。所提出的方法有两个主要步骤：1）诊断，和2）定位裂纹或其他损伤。该方法首先采用带有迁移学习的残差CNN对结构和构件的损伤进行分类。此步骤使用两个公共数据集执行损伤检测。第二步使用另一个具有U-Net结构的CNN来定位低分辨率图像上的裂缝。使用公共和自我收集的数据集的实现显示出有前途的性能，一直是一个挑战，在结构工程领域很长一段时间的问题，并表明所提出的方法可以执行检测和定位的结构损伤与可接受的精度。
Abstract. In this paper, two different convolutional neural networks (CNNs) are applied on images for automated structural damage detection (SDD) in earthquake damaged structures and cracking localization (e.g., detection of cracks, their widths and distributions) at various scales, such as pixel level, object level, and structural level. The proposed method has two main steps: 1) diagnosis, and 2) localization of cracking or other damage. At first a residual CNN with transfer learning is employed to classify the damage in the structures and structural components. This step performs damage detection using two public datasets. The second step uses another CNN with U-Net structure to locate the cracking on low resolution images. The implementations using public and self-collected datasets show promising performance for a problem that had remained a challenge in the structure engineering field for a long time and indicate that the proposed approach can perform detection and localization of structural damage with an acceptable accuracy.