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Authors: Kehinde Olufowobi and Nic Herndon

Affiliation: Department of Computer Science, East Carolina University, Greenville, U.S.A.

Keyword(s): Pavement Distress Detection, Object Detection, Homography Estimation, Neural Networks.

Abstract: Although advances in camera and sensing technology in the last decade helped propel the automation of pavement distress detection and characterization, increased equipment acquisition and running costs limit access to the most effective solutions. Furthermore, some of these advanced techniques require substantial human involvement to process and analyze data correctly. We propose a cost-effective, end-to-end automated approach to pavement condition assessment that employs a neural object detector to identify and measure instances of pavement distress in real time from oblique two-dimensional imagery acquired using an unmanned aerial vehicle. A state-of-the-art object detector architecture is applied to identify and localize pavement distress instances in these images. Camera data, information about Street View image acquisition conditions, and the principles of photogrammetry and planar homography are exploited to construct a mapping for translating pixel distances to real-world dist ances. This capability is integrated into the neural network inference process to derive an end-to-end system for real-time distress identification and measurement. (More)

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Paper citation in several formats:
Olufowobi, K. and Herndon, N. (2022). Towards a Low-cost Vision System for Real-time Pavement Condition Assessment. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-549-4; ISSN 2184-4313, SciTePress, pages 526-533. DOI: 10.5220/0010785900003122

@conference{icpram22,
author={Kehinde Olufowobi and Nic Herndon},
title={Towards a Low-cost Vision System for Real-time Pavement Condition Assessment},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2022},
pages={526-533},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010785900003122},
isbn={978-989-758-549-4},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Towards a Low-cost Vision System for Real-time Pavement Condition Assessment
SN - 978-989-758-549-4
IS - 2184-4313
AU - Olufowobi, K.
AU - Herndon, N.
PY - 2022
SP - 526
EP - 533
DO - 10.5220/0010785900003122
PB - SciTePress

- Science and Technology Publications, Lda.
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