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http://dx.doi.org/10.3745/KTSDE.2019.8.11.449

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

Park, Jae-Min (한경대학교 전기전자제어공학과)
Kim, Hyun-Seop (한경대학교 전기전자제어공학과)
Shin, Dong-Ho (한경대학교 전기전자제어공학과)
Park, Myeong-Suk (한경대학교 전기전자제어공학과)
Kim, Sang-Hoon (한경대학교 전기전자제어공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.8, no.11, 2019 , pp. 449-456 More about this Journal
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.
Keywords
Wall-Climbing Robot; Crack Detection Algorithms; Machine Learning; Localization;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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