Underwater vehicles have been widely used in the regular inspection of water-conveyance tunnels. However, for the long-distance water-conveyance tunnels, the high-precision navigation of underwater vehicles is very challenging as there is no satellite navigation signal. In this paper, we propose a tunnel joint extraction method based on deep learning, which can be used for the vision-assisted navigation of underwater vehicles in these tunnels. A light-weight semantic segmentation network for joint extraction is designed with a new architecture which overcomes the class imbalance problem and enables the real-time execution on a moderated GPU. Experimental results demonstrate that our method is effective and can significantly improve the navigation precision in long-distance water-conveyance tunnels.


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    Title :

    Joint Extraction Method for Underwater Vehicle Navigation in Long-Distance Water-Conveyance Tunnels


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Zhao, Guoteng (author) / Zhang, Hongliang (author) / Zhang, Yongqiang (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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