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Computer-Aided Road Inspection: Systems and Algorithms

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Recent Advances in Computer Vision Applications Using Parallel Processing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1073))

Abstract

Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and cumbersome. Also, manual road inspection results are qualitative and subjective, as they depend entirely on the inspector’s personal experience. Therefore, there is an ever-increasing need for automated road inspection systems. This chapter first compares the five most common road damage types. Then, 2-D/3-D road imaging systems are discussed. Finally, state-of-the-art machine vision and intelligence-based road damage detection algorithms are introduced.

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Notes

  1. 1.

    Source code is publicly available at https://github.com/ruirangerfan/unsupervised_disparity_map_segmentation.

  2. 2.

    A demo video can be found at https://vimeo.com/337886918.

  3. 3.

    Source code is publicly available at: https://github.com/hlwang1124/AAFramework.

  4. 4.

    https://sites.google.com/view/pothole-600.

  5. 5.

    3-D road point clouds are publicly available at https://github.com/ruirangerfan/stereo_pothole_datasets.

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Acknowledgements

This work was supported by the National Key R &D Program of China, under grant No. 2020AAA0108100, awarded to Prof. Qijun Chen. This work was also supported by the Fundamental Research Funds for the Central Universities, under projects No. 22120220184, No. 22120220214, and No. 2022-5-YB-08, awarded to Prof. Rui Fan.

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Fan, R., Guo, S., Wang, L., Junaid Bocus, M. (2023). Computer-Aided Road Inspection: Systems and Algorithms. In: Hosny, K.M., Salah, A. (eds) Recent Advances in Computer Vision Applications Using Parallel Processing . Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-031-18735-3_2

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