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Deep_Gabor_Neural_Network_for_Automatic_Detection_of_Mine-Like_Objects_in_Sonar_Imagery (1).pdf (6.42 MB)

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

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submitted on 2024-07-02, 05:41 and posted on 2024-07-02, 06:19 authored by Hoang Thanh Le, Son Lam Phung, Philip B. Chapple, Abdesselam Bouzerdoum, Christian H. Ritz, Le Chung Tran

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.

Other Information

Published in: IEEE Access
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1109/access.2020.2995390

Funding

Open Access funding provided by the NSW Defence Innovation Network and NSW State Government.

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU