A Study on Machine Learning Algorithm Suitable for Automatic Crack Detection in Wall-Climbing Robot


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 11, pp. 449-456, Nov. 2019
https://doi.org/10.3745/KTSDE.2019.8.11.449, Full Text:
Keywords: Wall-Climbing Robot, Crack Detection Algorithms, Machine Learning, localization
Abstract

This paper is a study on the construction of a wall-climbing mobile robot using vacuum suction and wheel-type movement, and a comparison of the performance of an automatic wall crack detection algorithm based on machine learning that is suitable for such an embedded environment. In the embedded system environment, we compared performance by applying recently developed learning methods such as YOLO for object learning, and compared performance with existing edge detection algorithms. Finally, in this study, we selected the optimal machine learning method suitable for the embedded environment and good for extracting the crack features, and compared performance with the existing methods and presented its superiority. In addition, intelligent problem - solving function that transmits the image and location information of the detected crack to the manager device is constructed.


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Cite this article
[IEEE Style]
J. Park, H. Kim, D. Shin, M. Park and S. Kim, "A Study on Machine Learning Algorithm Suitable for Automatic Crack Detection in Wall-Climbing Robot," KIPS Transactions on Software and Data Engineering, vol. 8, no. 11, pp. 449-456, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.11.449.

[ACM Style]
Jae-Min Park, Hyun-Seop Kim, Dong-Ho Shin, Myeong-Suk Park, and Sang-Hoon Kim. 2019. A Study on Machine Learning Algorithm Suitable for Automatic Crack Detection in Wall-Climbing Robot. KIPS Transactions on Software and Data Engineering, 8, 11, (2019), 449-456. DOI: https://doi.org/10.3745/KTSDE.2019.8.11.449.