@article {10.3844/ajeassp.2018.973.978,
article_type = {journal},
title = {Nondestructive Test Using a 3D Computer Vision System for Damage Detection of Structures},
author = {Szeto, Taylor M.V. and Yi, Sun},
volume = {11},
number = {2},
year = {2018},
month = {Jun},
pages = {973-978},
doi = {10.3844/ajeassp.2018.973.978},
url = {https://thescipub.com/abstract/ajeassp.2018.973.978},
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.},
journal = {American Journal of Engineering and Applied Sciences},
publisher = {Science Publications}
}