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Road Quality Classification

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Image Analysis and Processing – ICIAP 2022 (ICIAP 2022)

Abstract

Road quality significantly influences safety and comfort while driving. Especially for most kinds of two-wheelers, road damage is a real threat, where vehicle components and enjoyment are heavily impacted by road quality. This can be avoided by planning a route considering the surface quality. We propose a new publicly available and manually annotated dataset collected from Google Street View photos. This dataset is devoted to a road quality classification task considering six levels of damage. We evaluated some preprocessing methods such as shadow removal, CLAHE, and data augmentation. We adapted several pre-trained networks to classify road quality. The best performance was reached by MobileNet using augmented dataset (75.55%).

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Notes

  1. 1.

    https://rdd2020.sekilab.global/, https://github.com/sekilab/RoadDamageDetector.

  2. 2.

    https://github.com/lenoch0d/road-quality-classification.

  3. 3.

    https://github.com/vinthony/ghost-free-shadow-removal.

  4. 4.

    Including output classification layers for the ImageNet dataset.

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Acknowledgements

This research has been supported by SGS grant No. SGS20/213/OHK3/3T/18 and by GACR grant No. GA18-18080S.

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Correspondence to Magda Friedjungová .

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Lank, M., Friedjungová, M. (2022). Road Quality Classification. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_46

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  • DOI: https://doi.org/10.1007/978-3-031-06430-2_46

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