Road Surface Damages

Citation Author(s):
Gilberto
Ochoa Ruiz
Tecnologico de Monterrey
Andres Alonso
Angulo Murillo
Universidad Autonoma de Guadalajara
Submitted by:
Gilberto Ochoa-Ruiz
Last updated:
Tue, 01/21/2020 - 14:54
DOI:
10.21227/nbdy-r451
Links:
License:
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Abstract 

Abstract: 

Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection. Such dataset could be used in a great variety of applications; herein, it is intended to serve as the acquisition component of a physical asset management tool which can aid governments agencies for planning purposes, or by infrastructure maintenance companies. described in https://github.com/sekilab/RoadDamageDetector/ and available here https://mycityreport.s3-ap-northeast-1.amazonaws.com/02_RoadDamageDatase...), by incoporating more examples of potholes and longitudinal and alligator cracks

Instructions: 

Information about the dataset can be found at https://arxiv.org/abs/1909.08991, please cite the paper as it is if you use the dataset. Annotation for boudning boxes are provided to train YOLO based detectors

Comments

Nice dataset

Submitted by Gilberto Ochoa-Ruiz on Tue, 12/10/2019 - 15:36
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