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http://dx.doi.org/10.12815/kits.2019.18.6.155

Road Crack Detection based on Object Detection Algorithm using Unmanned Aerial Vehicle Image  

Kim, Jeong Min (Dept. of Urban Eng., Natl Univ. of Hanbat)
Hyeon, Se Gwon (Dept. of Urban Eng., Natl Univ. of Hanbat)
Chae, Jung Hwan (Dept. of Urban Eng., Natl Univ. of Hanbat)
Do, Myung Sik (Dept. of Urban Eng., Natl Univ. of Hanbat)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.18, no.6, 2019 , pp. 155-163 More about this Journal
Abstract
This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.
Keywords
Deep Learning; Road Crack; Drone; Tiny-YOLO-V2; Faster-RCNN;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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