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Flooded Road Detection from Driving Recorder: Training Deep Net for Rare Event Using GANs Semantic Information

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Abstract

It is important for traffic management to understand unusual conditions or road abnormalities caused by natural disasters (such as an earthquake or heavy rain) or traffic congestion caused by special events (such as festivals at tourist spots). Among these, we focused on flooded roads as unusual events and proposed a method to detect it automatically, using deep-learning methods from driving videos. Because such unusual events rarely occur, the amount of training data for deep learning is usually insufficient. Therefore, we propose a data-augmentation approach using Generative Adversarial Networks (GANs) to solve the problem. To effectively augment the data, we propose a multi-domain image-to-image transformation by GANs. In addition, to increase the robustness of the image transformation, we newly introduce semantic information. We synthesized a new dataset using GANs and verified the performance of our method by detecting flooded scenes.

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Acknowledgements

This research was, in part, supported by CART (Committee on Advanced Road Technology), Ministry of Land, Infrastructure, Transport and Tourism and JSPS/KAKENHI 16KK0151, 18H04119 and 18 K19824.

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Authors and Affiliations

  1. Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

    Sho Nakamura & Hiroshi Kawasaki

  2. Institute of Industrial Science, The University of Tokyo, 4 Chome-6-1 Komaba, Meguro City, Tokyo, 153-8505, Japan

    Shintaro Ono

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  1. Sho Nakamura
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  2. Shintaro Ono
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  3. Hiroshi Kawasaki
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Correspondence to Sho Nakamura.

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Nakamura, S., Ono, S. & Kawasaki, H. Flooded Road Detection from Driving Recorder: Training Deep Net for Rare Event Using GANs Semantic Information. Int. J. ITS Res. 19, 1–11 (2021). https://doi.org/10.1007/s13177-019-00219-9

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  • DOI: https://doi.org/10.1007/s13177-019-00219-9

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