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Research Article · Open access · Published 2 June 2018

Nondestructive Test Using a 3D Computer Vision System for Damage Detection of Structures

Taylor M.V. Szeto1 & Sun Yi1

American Journal of Engineering and Applied Sciences, Volume 11, Issue 2, pp. 973–978 · doi.org/10.3844/ajeassp.2018.973.978

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Abstract

Structures that do not have a definitive map nor a clearly known state of health may exist underground, beyond our reach and in unsuitable environments. Nuclear facilities contain underground tunnels that exhaust hazardous gases. Industry has miles of sewage lines beneath it that emit flammable and toxic gases. Regular inspection and maintenance is an essential part of failure prevention, however, these fatal environments have prohibited proper inspection of such infrastructure. Thus, the future of computer vision is vital to quality inspection. A strategically designed robot can be trained to visually inspect any structure, detect if there is a damage and decide if the damage is critical. Likened to any good inspector, the robot must be trained to investigate the nature of the damage and to alert the user of potential failures. This article discusses robotic training to detect damage in concrete structures and make decisions to the significance and impact of the defect.

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Open access. This article is licensed under a Creative Commons Attribution 3.0 Licence (CC BY 3.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.

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Szeto, T. M. & Yi, S. (2018). Nondestructive Test Using a 3D Computer Vision System for Damage Detection of Structures. American Journal of Engineering and Applied Sciences, 11(2), 973–978. https://doi.org/10.3844/ajeassp.2018.973.978

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Nondestructive Test Using a 3D Computer Vision System for Damage Detection of StructuresAmerican Journal of Engineering and Applied Sciences · Volume 11, Issue 2 · pp. 973–978 · CC BY 3.0Download