Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.


    Access

    Download


    Export, share and cite



    Title :

    BgCut: Automatic Ship Detection from UAV Images


    Contributors:
    Chao Xu (author) / Dongping Zhang (author) / Zhengning Zhang (author) / Zhiyong Feng (author)


    Publication date :

    2014




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Automatic ship detection in SAR satellite images: Performance assessment

    Stasolla, Mattia / Santamaria, Carlos / Mallorqui, Jordi J. et al. | IEEE | 2015


    Automatic Ship Wake Detection from Sentinel-2 Images by Deep Learning

    Prete, Roberto Del / Renga, Alfredo / Graziano, Maria Daniela et al. | TIBKAT | 2023



    Automatic recognition of ISAR ship images

    Musman, S. / Kerr, D. / Bachmann, C. | Tema Archive | 1996


    Automatic recognition of ISAR ship images

    Musman, S. / Kerr, D. / Bachmann, C. | IEEE | 1996