상세정보
학술지
PotholeEye+: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance
- 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
- 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.
- KSP 제안 키워드
- Average speed, GPS Receiver, Intrusion detection system(IDS), Mobile system, Pavement distress, Real-Time, deep learning(DL), smart maintenance
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