BROWSE
Detail
Journal Article
PotholeEye+: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance
- Authors
- Juyoung Park, Jung Hee Lee, Junseong Bang
- Issue Date
- 2021-05
- Citation
- Computer Modeling in Engineering & Sciences, v.127, no.3, pp.965-976
- ISSN
- 1526-1492
- Publisher
- Tech Science Press
- Language
- English
- Type
- Journal Article
- Abstract
- 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 Keywords
- Average speed, GPS Receiver, Intrusion Detection Systems(IDS), Mobile system, Pavement distress, deep learning(DL), real time, smart maintenance
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
