Y
Young-Jin Cha
Researcher at University of Manitoba
Publications - 71
Citations - 5738
Young-Jin Cha is an academic researcher from University of Manitoba. The author has contributed to research in topics: Structural health monitoring & Convolutional neural network. The author has an hindex of 24, co-authored 64 publications receiving 3428 citations. Previous affiliations of Young-Jin Cha include City University of New York & Massachusetts Institute of Technology.
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Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks
TL;DR: This article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features, and shows quite better performances and can indeed find concrete cracks in realistic situations.
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Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types
TL;DR: A framework for quasi real-time damage detection on video using the trained networks is developed and the robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures.
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Modal identification of simple structures with high-speed video using motion magnification
TL;DR: In this article, motion magnification has been developed for visualizing exaggerated versions of small displacements with an extension of the methodology to obtain the optical flow to measure displacements, which can be extended to modal identification in structures and the measurement of structural vibrations.
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Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging
Dong Ho Kang,Young-Jin Cha +1 more
TL;DR: In this article, the authors used visual inspection for structural health monitoring and contact sensors on structures for monitoring a building's structural health. But, the assessment conducted by trained inspectors or using contact sensors was ineffective.
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Vision-based detection of loosened bolts using the Hough transform and support vector machines
TL;DR: In this article, the horizontal and vertical lengths of the bolt head were calculated automatically using the Hough transform and other image processing techniques, and a linear support vector machine was trained with the aforementioned features, thereby building a robust classifier capable of automatically differentiating tight bolts from loose bolts.