Unmanned surface vehicles (USVs) are receiving increasing attention in recent years from both academia and industry. To make a high-level autonomy for USVs, the environment situational awareness is a key capability. However, due to the richness of the features in marine environments, as well as the complexity of the environment influenced by sun glare and sea fog, the development of a reliable situational awareness system remains a challenging problem that requires further studies. This paper, therefore, proposes a new deep semantic segmentation model together with a Simple Linear Iterative Clustering (SLIC) algorithm, for an accurate perception for various maritime environments. More specifically, powered by the SLIC algorithm, the new segmentation model can achieve refined results around obstacle edges and improved accuracy for water surface obstacle segmentation. The overall structure of the new model employs an encoder–decoder layout, and a superpixel refinement is embedded before final outputs. Three publicly available maritime image datasets are used in this paper to train and validate the segmentation model. The final output demonstrates that the proposed model can provide accurate results for obstacle segmentation.


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    Title :

    Deep Learning-Based Maritime Environment Segmentation for Unmanned Surface Vehicles Using Superpixel Algorithms


    Contributors:
    Xue, H (author) / Chen, X (author) / Zhang, R (author) / WU, P (author) / Li, X (author) / Liu, Y (author)

    Publication date :

    2021-11-25


    Remarks:

    Journal of Marine Science and Engineering , 9 (12) , Article 1329. (2021)


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    The Operation of Unmanned Air Vehicles in the Maritime Environment

    Pacey, P. J. / University of Bristol | British Library Conference Proceedings | 1993


    The Operation of Unmanned Air Vehicles in the Maritime Environment

    Pacey, P. J. / University of Bristol | British Library Conference Proceedings | 1993