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, Detection Systems(IDS), GPS receiver, Mobile system, Pavement distress, Real-time, deep learning(DL), smart maintenance
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
