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Smart pothole detection system using vehicle-mounted sensors and machine learning

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Abstract

Road networks are critical assets supporting economies and communities. Despite budget and time constraints, road authorities strive to maintain them to ensure safety, ongoing service, and economic productivity. This paper proposes a virtual road network inspector (VRNI), which continuously monitors road conditions and provides decision support to managers and engineers. VRNI uses acceleration data from vehicle-mounted sensors to assess road conditions. It proposes a novel road damage detection method based on two adaptive one-class support vector machine models, which were applied on the vertical and lateral acceleration data. We evaluated this method on data from a real deployment on school buses in New South Wales, Australia. Experimental results show that our method consistently detects 97.5% of the road damage with a 4% false alarm rate that relate to benign anomalies such as expansion joints.

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Notes

  1. Video recordings were only used as validation in the prototype testing. VRNI does not require and will not use video in a production deployment.

  2. As mentioned earlier, in a real-world deployment VRNI might be deployed on a route, which at first may have no damage at all, i.e., only one class of data is available.

  3. <url to download the data> will be made available upon paper publication.

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Authors and Affiliations

  1. Faculty of Engineering and IT, The University of Sydney, Camperdown, Australia

    Ali Anaissi

  2. Data61|CSIRO, Eveleigh, Canberra, Australia

    Nguyen Lu Dang Khoa, Thierry Rakotoarivelo & Yang Wang

  3. School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia

    Mehrisadat Makki Alamdari

Authors
  1. Ali Anaissi
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  2. Nguyen Lu Dang Khoa
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  3. Thierry Rakotoarivelo
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  4. Mehrisadat Makki Alamdari
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  5. Yang Wang
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Correspondence to Ali Anaissi.

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Anaissi, A., Khoa, N.L.D., Rakotoarivelo, T. et al. Smart pothole detection system using vehicle-mounted sensors and machine learning. J Civil Struct Health Monit 9, 91–102 (2019). https://doi.org/10.1007/s13349-019-00323-0

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  • DOI: https://doi.org/10.1007/s13349-019-00323-0

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