Large-scale Continual Road Inspection: Visual Infrastructure Assessment in the Wild
Ke Ma, Minh Hoai and Dimitris Samaras
Abstract
This work develops a method to inspect the quality of pavement conditions based
on images captured from moving vehicles. This task is challenging because the appearance of road surfaces varies tremendously, depending on the construction materials
(e.g., concrete, asphalt), the weather conditions (e.g., rain, snow), the illumination conditions (e.g., sunny, shadow), and the interference of other structures (e.g., manholes,
road marks). This problem is amplified by the lack of a sufficiently large and diverse
dataset for training a pavement classifier. Our first contribution in this paper is the development of a method to create a large-scale dataset of pavement images. Specifically,
using map and GPS information, we match the ratings by government inspectors found in
public databases to Google Street View images, creating a dataset containing more than
700K images from 70K street segments. We use the dataset to develop a deep-learning
method for road assessment, which is based on Convolutional Neural Networks, Fisher
Vector encoding, and UnderBagging random forests. This method achieves an accuracy
of 58.
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DOI
10.5244/C.31.151
https://dx.doi.org/10.5244/C.31.151
Citation
Ke Ma, Minh Hoai and Dimitris Samaras. Large-scale Continual Road Inspection: Visual Infrastructure Assessment in the Wild. In T.K. Kim, S. Zafeiriou, G. Brostow and K. Mikolajczyk, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 151.1-151.13. BMVA Press, September 2017.
Bibtex
@inproceedings{BMVC2017_151,
title={Large-scale Continual Road Inspection: Visual Infrastructure Assessment in the Wild},
author={Ke Ma, Minh Hoai and Dimitris Samaras},
year={2017},
month={September},
pages={151.1-151.13},
articleno={151},
numpages={13},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
doi={10.5244/C.31.151},
isbn={1-901725-60-X},
url={https://dx.doi.org/10.5244/C.31.151}
}