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학술지 PotholeEye+: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance
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저자
박주영, 이정희, 방준성
발행일
202105
출처
Computer Modeling in Engineering & Sciences, v.127 no.3, pp.965-976
ISSN
1526-1492
출판사
Tech Science Press
DOI
https://dx.doi.org/10.32604/cmes.2021.014669
초록
We propose a mobile system, called PotholeEye+, for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video. PotholeEye+ pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. Every day for a year, we have tested PotholeEye+ on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on. Consequently, PotholeEye+ detected the pavement distress with accuracy of 92%, precision of 87% and recall 74% averagely during driving at an average speed of 110 km/h on a real highway.
키워드
Classification, Convolutional neural network, Deep learning, Detection, Pavement distress, Video analysis
KSP 제안 키워드
Average speed, Convolution neural network(CNN), GPS Receiver, Intrusion detection system(IDS), Mobile system, Pavement distress, Real-Time, deep learning(DL), smart maintenance, video analysis
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