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http://dx.doi.org/10.9723/jksiis.2019.24.3.023

Crack Detection on the Road in Aerial Image using Mask R-CNN  

Lee, Min Hye (군산대학교 컴퓨터정보통신공학부)
Nam, Kwang Woo (군산대학교 컴퓨터정보통신공학부)
Lee, Chang Woo (군산대학교 컴퓨터정보통신공학부)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.24, no.3, 2019 , pp. 23-29 More about this Journal
Abstract
Conventional crack detection methods have a problem of consuming a lot of labor, time and cost. To solve these problems, an automatic detection system is needed to detect cracks in images obtained by using vehicles or UAVs(unmanned aerial vehicles). In this paper, we have studied road crack detection with unmanned aerial photographs. Aerial images are generated through preprocessing and labeling to generate morphological information data sets of cracks. The generated data set was applied to the mask R-CNN model to obtain a new model in which various crack information was learned. Experimental results show that the cracks in the proposed aerial image were detected with an accuracy of 73.5% and some of them were predicted in a certain type of crack region.
Keywords
object detection; crack; aerial image; GeoAI; mask R-CNN;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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