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Vision for Road Inspection

Srivatsan Varadharajan, Sobhagya Jose, Karan Sharma, Lars Wander and Christoph Mertz
Journal Article, Carnegie Mellon University, Proceedings of WACV 2014: IEEE Winter Conference on Applications of Computer Vision, March, 2014

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Abstract

Road surface inspection in cities is for the most part, a task performed manually. Being a subjective and labor intensive process, it is an ideal candidate for automation. We propose a solution based on computer vision and data- driven methods to detect distress on the road surface. Our method works on images collected from a camera mounted on the windshield of a vehicle. We use an automatic pro- cedure to select images suitable for inspection based on lighting and weather conditions. From the selected data we segment the ground plane and use texture, color and loca- tion information to detect the presence of pavement distress. We describe an over-segmentation algorithm that identifies coherent image regions not just in terms of color, but also texture. We also discuss the problem of learning from unre- liable human-annotations and propose using a weakly su- pervised learning algorithm (Multiple Instance Learning) to train a classifier. We present results from experiments comparing the performance of this approach against multi- ple individual human labelers, with the ground-truth labels obtained from an ensemble of other human labelers. Fi- nally, we show results of pavement distress scores computed using our method over a subset of a citywide road network.

BibTeX Reference
@article{Varadharajan-2014-7844,
title = {Vision for Road Inspection},
author = {Srivatsan Varadharajan and Sobhagya Jose and Karan Sharma and Lars Wander and Christoph Mertz},
booktitle = {Proceedings of WACV 2014: IEEE Winter Conference on Applications of Computer Vision},
school = {Robotics Institute , Carnegie Mellon University},
month = {March},
year = {2014},
address = {Pittsburgh, PA},
}
2017-09-13T10:39:03+00:00