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Deep Gabor Neural Network for Automatic Detection of Mine-like Objects in Sonar Imagery

Journal Article


Abstract


  • With the advances in sonar imaging technology, sonar imagery has increasingly been used for

    oceanographic studies in civilian and military applications. High-resolution imaging sonars can be mounted

    on various survey platforms, typically autonomous underwater vehicles, which provide enhanced speed and

    improved data quality with long-range support. This paper addresses the automatic detection of mine-like

    objects using sonar images. The proposed Gabor-based detector is designed as a feature pyramid network

    with a small number of trainable weights. Our approach combines both semantically weak and strong

    features to handle mine-like objects at multiple scales effectively. For feature extraction, we introduce

    a parameterized Gabor layer which improves the generalization capability and computational efficiency.

    The steerable Gabor filtering modules are embedded within the cascaded layers to enhance the scale and

    orientation decomposition of images. The entire deep Gabor neural network is trained in an end-to-end

    manner from input sonar images with annotated mine-like objects. An extensive experimental evaluation

    on a real sonar dataset shows that the proposed method achieves competitive performance compared to the

    existing approaches.

Publication Date


  • 2020

Citation


  • H. Thanh. Le, S. Lam. Phung, P. B. Chapple, A. Bouzerdoum, C. H. Ritz & L. Tran, "Deep Gabor Neural Network for Automatic Detection of Mine-like Objects in Sonar Imagery," IEEE Access, pp. 1-14, 2020.

Number Of Pages


  • 13

Start Page


  • 1

End Page


  • 14

Place Of Publication


  • United States

Abstract


  • With the advances in sonar imaging technology, sonar imagery has increasingly been used for

    oceanographic studies in civilian and military applications. High-resolution imaging sonars can be mounted

    on various survey platforms, typically autonomous underwater vehicles, which provide enhanced speed and

    improved data quality with long-range support. This paper addresses the automatic detection of mine-like

    objects using sonar images. The proposed Gabor-based detector is designed as a feature pyramid network

    with a small number of trainable weights. Our approach combines both semantically weak and strong

    features to handle mine-like objects at multiple scales effectively. For feature extraction, we introduce

    a parameterized Gabor layer which improves the generalization capability and computational efficiency.

    The steerable Gabor filtering modules are embedded within the cascaded layers to enhance the scale and

    orientation decomposition of images. The entire deep Gabor neural network is trained in an end-to-end

    manner from input sonar images with annotated mine-like objects. An extensive experimental evaluation

    on a real sonar dataset shows that the proposed method achieves competitive performance compared to the

    existing approaches.

Publication Date


  • 2020

Citation


  • H. Thanh. Le, S. Lam. Phung, P. B. Chapple, A. Bouzerdoum, C. H. Ritz & L. Tran, "Deep Gabor Neural Network for Automatic Detection of Mine-like Objects in Sonar Imagery," IEEE Access, pp. 1-14, 2020.

Number Of Pages


  • 13

Start Page


  • 1

End Page


  • 14

Place Of Publication


  • United States