A review of the research and application of deep learning-based computer vision in structural damage detection

Zhang Lingxin; Shen Junkai; Zhu Baijie

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A review of the research and application of deep learning-based computer vision in structural damage detection

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Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detection,detecting damages with high efficiency and accuracy is the most popular research direction in civil engineering.Computer vision(CV)technology and deep learning(DL)algorithms are considered as promising tools to address the aforementioned challenges.The paper aims to systematically summarized the research and applications of DL-based CV technology in the field of damage detection in recent years.The basic concepts of DL-based CV technology are introduced first.The implementation steps of creating a damage detection dataset and some typical datasets are reviewed.CV-based structural damage detection algorithms are divided into three categories,namely,image classification-based(IC-based)algorithms,object detection-based(OD-based)algorithms,and semantic segmentation-based(SS-based)algorithms.Finally,the problems to be solved and future research directions are discussed.The foundation for promoting the deep integration of DL-based CV technology in structural damage detection and structural seismic damage identification has been laid.

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来源
《地震工程与工程振动：英文版》 |2022年第1期|1-21|共21页
作者
Zhang Lingxin; Shen Junkai; Zhu Baijie;
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作者单位

Institute of Engineering Mechanics;

China Earthquake Administration;

Harbin 150080;

China;

Key Laboratory of Earthquake Engineering and Engineering Vibration of China Earthquake Administration;

Harbin 150080;

China;

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原文格式 PDF
正文语种 chi
中图分类计算技术、计算机技术;
关键词
deep learning; damage detection; computer vision; loss assessment;

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专利

1. Research on Computer Vision Detection Technology and Applications on Machinery Manufacturing and Automation [J] . Hui Liu1 . International Journal of Technology Management . 2016,第005期
2. Application of clan member signal method in structural damage detection [J] . 郭迅 ,B.F.Spencer ,卢书楠 . 地震工程与工程振动：英文版 . 2007,第1期
3. 3D computer vision using Point Grey Research stereo vision cameras [C] . Don Murray . 第二届立体图像技术及其应用(国际)研讨会 . 2007
4. The Surface Defects Detection Algorithm Based on Computer Vision for Connectors [A] . Alimbekov Abay . 2017

A review of the research and application of deep learning-based computer vision in structural damage detection

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