Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery
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 a...
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doaj-a5195fcc847746e18ae97422a7b1113a2021-03-30T03:00:16ZengIEEEIEEE Access2169-35362020-01-018941269413910.1109/ACCESS.2020.29953909095329Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar ImageryHoang Thanh Le0https://orcid.org/0000-0003-2762-5708Son Lam Phung1Philip B. Chapple2Abdesselam Bouzerdoum3Christian H. Ritz4Le Chung Tran5https://orcid.org/0000-0002-2677-8721School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, AustraliaDefence Science and Technology, Liverpool, NSW, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, AustraliaWith 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.https://ieeexplore.ieee.org/document/9095329/Gabor neural network detectorGabor layerside-scan sonarmine-like objects |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hoang Thanh Le Son Lam Phung Philip B. Chapple Abdesselam Bouzerdoum Christian H. Ritz Le Chung Tran |
spellingShingle |
Hoang Thanh Le Son Lam Phung Philip B. Chapple Abdesselam Bouzerdoum Christian H. Ritz Le Chung Tran Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery IEEE Access Gabor neural network detector Gabor layer side-scan sonar mine-like objects |
author_facet |
Hoang Thanh Le Son Lam Phung Philip B. Chapple Abdesselam Bouzerdoum Christian H. Ritz Le Chung Tran |
author_sort |
Hoang Thanh Le |
title |
Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery |
title_short |
Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery |
title_full |
Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery |
title_fullStr |
Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery |
title_full_unstemmed |
Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery |
title_sort |
deep gabor neural network for automatic detection of mine-like objects in sonar imagery |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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. |
topic |
Gabor neural network detector Gabor layer side-scan sonar mine-like objects |
url |
https://ieeexplore.ieee.org/document/9095329/ |
work_keys_str_mv |
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