
@Article{cmes.2021.014669,
AUTHOR = {Juyoung Park, Jung Hee Lee, Junseong Bang},
TITLE = {PotholeEye<sup>+</sup>: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {127},
YEAR = {2021},
NUMBER = {3},
PAGES = {965--976},
URL = {http://www.techscience.com/CMES/v127n3/42596},
ISSN = {1526-1506},
ABSTRACT = {<p>We propose a mobile system, called PotholeEye<sup>+</sup>, for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video. PotholeEye<sup>+</sup> 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<sup>+</sup> on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on. Consequently, PotholeEye<sup>+</sup> 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.</p>
},
DOI = {10.32604/cmes.2021.014669}
}



