Prof. Young-Jin Cha Profile

Prof. Young-Jin Cha

Assistant Professor at Univ of Manitoba

SPIE Involvement:

Author

Publications (9)

Proceedings Article | 22 April 2020 Presentation + Paper

Presentation + Paper

Heat loss detection using thermal imaging by a small UAV prototype

Rahmat Ali, Jiangyu Zeng, Miroslava Kavgic, Young-Jin Cha

Proceedings Volume 11382, 113820S (2020) https://doi.org/10.1117/12.2557902

KEYWORDS: Infrared sensors, Unmanned aerial vehicles, Buildings, Cameras, Thermography, Image sensors, Forward looking infrared, Video, Infrared imaging, Infrared cameras

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Proceedings Article | 22 April 2020 Presentation + Paper

Presentation + Paper

Deep learning-based crack detection in a concrete tunnel structure using multispectral dynamic imaging

Rahmat Ali, Jiangyu Zeng, Young-Jin Cha

Proceedings Volume 11382, 1138203 (2020) https://doi.org/10.1117/12.2557900

KEYWORDS: Multispectral imaging, Neural networks, Damage detection, Image processing, Convolution, Cameras, Bridges, Thermography, Network architectures, Alternate lighting of surfaces

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Proceedings Article | 3 April 2018 Paper

Paper

Automated volumetric damage detection and quantification using region-based convolution neural networks and an inexpensive depth camera

Gustavo Beckman, Dimos Polyzois, Young-Jin Cha

Proceedings Volume 10598, 105980W (2018) https://doi.org/10.1117/12.2295959

KEYWORDS: Sensors, Damage detection, Cameras, Data acquisition, RGB color model, Clouds, Convolution, Neural networks, Infrared sensors, 3D image processing

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Proceedings Article | 27 March 2018 Paper

Paper

Deep faster R-CNN-based automated detection and localization of multiple types of damage

Gahyun Suh, Young-Jin Cha

Proceedings Volume 10598, 105980T (2018) https://doi.org/10.1117/12.2295954

KEYWORDS: Corrosion, Damage detection, Optical inspection, Convolutional neural networks, Machine vision, Computer vision technology, Structural health monitoring, Bridges

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Proceedings Article | 27 March 2018 Paper

Paper

Automated damage-sensitive feature extraction using unsupervised convolutional neural networks

Zilong Wang, Young-jin Cha

Proceedings Volume 10598, 105981J (2018) https://doi.org/10.1117/12.2295966

KEYWORDS: Damage detection, Feature extraction, Bridges, Continuous wavelet transforms, Wavelets, Convolutional neural networks, Machine learning

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Proceedings Article | 27 March 2018 Paper

Paper

Vision-based concrete crack detection technique using cascade features

Rahmat Ali, Dharshan Lokekere Gopal, Young-Jin Cha

Proceedings Volume 10598, 105980L (2018) https://doi.org/10.1117/12.2295962

KEYWORDS: Detection and tracking algorithms, Image processing, Facial recognition systems, Structural health monitoring, Inspection, Machine vision, Computer vision technology

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Proceedings Article | 27 March 2018 Paper

Paper

Damage detection with an autonomous UAV using deep learning

Dongho Kang, Young-Jin Cha

Proceedings Volume 10598, 1059804 (2018) https://doi.org/10.1117/12.2295961

KEYWORDS: Unmanned aerial vehicles, Global Positioning System, Damage detection, Structural health monitoring, Sensors, Inspection, Cameras, Video, Optical inspection

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Proceedings Article | 27 March 2018 Paper

Paper

Identification of large-scale systems with noisy data using an iterated cubature unscented Kalman filter

Esmaeil Ghorbani, Young-Jin Cha

Proceedings Volume 10598, 105981R (2018) https://doi.org/10.1117/12.2295952

KEYWORDS: Filtering (signal processing), Nonlinear filtering, System identification, Systems modeling, Complex systems, Structural health monitoring

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Proceedings Article | 27 March 2018 Paper

Paper

Automated air-coupled impact echo based non-destructive testing using machine learning

Tyler Epp, Dagmar Svecova, Young-Jin Cha

Proceedings Volume 10598, 105981M (2018) https://doi.org/10.1117/12.2295947

KEYWORDS: Machine learning, Sensors, Structural health monitoring, Artificial neural networks, Visualization, Signal processing, Nondestructive evaluation, Wavelet transforms, Civil engineering

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Showing 5 of 9 publications

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