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.
Highlights
Over the past two decades, autonomous underwater vehicles (AUVs) have been increasingly used to survey the seabed
SONAR DATA ACQUISITION AND ANNOTATION The sonar data were provided by the Defence Science and Technology (DST) Group in a naval mine-shape recovery operation in Australia [40]
A Marine Sonic Technology (MST) side-scan sonar with dual frequencies was employed for data acquisition
Summary
Over the past two decades, autonomous underwater vehicles (AUVs) have been increasingly used to survey the seabed. Le et al.: Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery. Further studies conducted on macaques [5], [6] and humans [7], [8] interpreted the computational models of the primary visual cortex as a bank of Gabor filters with selective orientation, spatial frequency, phase and bandwidth Such orientation-sensitive functions can be learned by many machine learning algorithms when applied to natural images. We propose a Gabor-based neural network architecture for MLO detection in sonar imagery. We propose a new deep Gabor neural network (GNN) for MLO detection in sonar imagery.
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