Multioutput Image Classification to Support Postearthquake Reconnaissance
Multioutput Image Classification to Support Postearthquake Reconnaissance
复制标题
DOI：
10.1061/(asce)cf.1943-5509.0001755
复制
发表时间：
2022-12
影响因子：
2.5
通讯作者：
Ju An Park;Xiaoyu Liu;C. Yeum;S. Dyke;Max Midwinter;Jongseong Choi;Zhiwei Chu;T. Hacker;Bedrich Benes
中科院分区：
文献类型：
--
作者：
Ju An Park;Xiaoyu Liu;C. Yeum;S. Dyke;Max Midwinter;Jongseong Choi;Zhiwei Chu;T. Hacker;Bedrich Benes
在灾害发生后，侦察小组收集了大量图像，以记录建筑物的事后状态，并评估其性能，改进设计程序和规范。这些数据中的大多数被捕获为图像并手动标记。这个高度重复的任务需要大量的领域专业知识和时间。深度学习的进步使研究人员能够快速分类侦察图像。到目前为止，这些分类方法仅限于一个简单的分类模式，其中所有类要么互斥，要么独立。迄今为止，还没有一种有效的包含许多类的复杂模式的分类系统来支持地震侦察。为了解决这一问题，本文引入了一种综合分类模式和多输出深度卷积神经网络（DCNN）模型，用于震后图像的快速分类。与以往的工作不同，本文训练了一个具有层次感知预测的单一多输出DCNN分类模型，以实现图像的快速组织。通过使用f1评分与多标签和多类别模型进行比较，验证了所提出的多输出模型的性能。结果表明，多输出模型优于其他模型。然后，将多输出模型部署到基于web的自动侦察图像组织者平台上，该平台可用于方便地组织地震侦察图像。DOI: 10.1061 /(陈纯)cf.1943 - 5509.0001755。©2022美国土木工程师学会。
: After hazard events, large numbers of images are collected by reconnaissance teams to document the post-event state of structures, and to assess their performance and improve design procedures and codes. The majority of these data are captured as images and manually labeled. This highly repetitive task requires considerable domain expertise and time. Advances in deep learning have enabled researchers to rapidly classify reconnaissance images. Thus far, these classification methods are limited to a simple classification schema in which the classes are all either mutually exclusive or independent. To date, an efficient classification system of a complex schema containing many classes arranged in a multi-level hierarchical structure is not available to support earthquake reconnaissance. To address this gap, this paper introduces a comprehensive classification schema and a multi-output deep convolutional neural network (DCNN) model for rapid postearthquake image classification. In contrast to past work, herein a single multi-output DCNN classification model with a hierarchy-aware prediction was trained to enable the rapid organization of images. The performance of the proposed multi-output model was validated through comparisons with multi-label and multi-class models using an F1-score. As result, the multi-output model outperformed other models. Then, the multi-output model was deployed to a web-based platform called the Automated Reconnaissance Image Organizer, which can be used to easily organize earthquake reconnaissance images. DOI: 10.1061/(ASCE)CF.1943-5509.0001755. © 2022 American Society of Civil Engineers.