Classification of anti-submarine warfare sonar targets using a deep neural network

Berg, Henrik; Hjelmervik, Karl Thomas

doi:10.1109/oceans.2018.8604847

Cited by 22 publications

(14 citation statements)

References 8 publications

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“…This is a critical issue in the classification of underwater acoustic data, since most datasets are not publicly available, owing to the financial and technical complexity in obtaining such data and also to their potential defence-sensitive information. Therefore, much work in this area is conducted using synthetic data only [ 124 ], or on a limited set of real data augmented with semisynthetic examples for training [ 62 , 128 , 139 ]. There are, however, a few datasets commonly used in the literature, that are summarised in Table 6 .…”

Section: Datasets and Data-augmentation Methodsmentioning

confidence: 99%

A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

Domingos

Santos

Skelton

et al. 2022

Sensors

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This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the identification of objects of interest from active sonar (such as minelike objects, human figures or debris of wrecked ships). Not only is the contribution of this work to provide a systematic description of the state of the art of this field, but also to identify five main ingredients in its current development: the application of deep-learning methods using convolutional layers alone; deep-learning methods that apply biologically inspired feature-extraction filters as a preprocessing step; classification of data from frequency and time–frequency analysis; methods using machine learning to extract features from original signals; and transfer learning methods. This paper also describes some of the most important datasets cited in the literature and discusses data-augmentation techniques. The latter are used for coping with the scarcity of annotated sonar datasets from real maritime missions.

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Section: Datasets and Data-augmentation Methodsmentioning

confidence: 99%

A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

Domingos

Santos

Skelton

et al. 2022

Sensors

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“…The use of CNN also showed advantage for reducing the high false alarm rates in low-frequency active sonar operating in shallow water [33]. Current solutions include post-detection classification [34], classifying the output of the bearing-time matched filter using a deep neural network [35], of spectrogram classification of suspected targets by CNN [36].…”

Section: Related Workmentioning

confidence: 99%

“…While these neural network-based classifiers show remarkable results, their effective application requires large annotated datasets, which, at sea, are hard to get [35]. To circumvent this limitation, data augmentation [35] or generation of synthetic datasets [37], [36] can be used. The solution we propose here is to train the deep network using synthetic samples, or samples from real sea experiments for which ground truth information is known.…”

Section: Related Workmentioning

confidence: 99%

Detecting Submerged Objects Using Active Acoustics and Deep Neural Networks: A Test Case for Pelagic Fish

Testolin

Kipnis

Diamant

2022

IEEE Trans. on Mobile Comput.

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The accurate detection and quantification of submerged targets has been recognized as a key challenge in marine exploration, one that traditional census approaches cannot handle efficiently. Here we present a deep learning approach to detect the pattern of a moving fish from the reflections of an active acoustic emitter. To allow for real-time detection, we use a convolutional neural network, which provides the simultaneous labeling of a large buffer of signal samples. This allows to capture the structure of the reflecting signal from the moving target and to separate it from clutter reflections. We evaluate system performance both on synthetic (simulated) data, as well as on real data recorded over 50 sea experiments in a variety of sea conditions. When tested on real signals, the network trained on simulated patterns showed non-trivial detection capabilities, suggesting that transfer learning can be a viable approach in these scenarios, where tagged data is often lacking. However, training the network directly on the real reflections with data augmentation techniques allowed to reach a more favorable precision-recall trade-off, approaching an ideal detection bound. We also evaluate an alternative model based on recurrent neural networks which, despite exhibiting slightly inferior performance, could be applied in scenarios requiring on-line processing of the reflection sequence.

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“…At present, for the lack of dataset, transfer learning 5 –7 can be used to train model on a dataset with a large number of land or air targets and then transfer the model to the underwater target field. Generative adversarial network (GAN) 8,9 is a new method, which can autonomously generate underwater target images to increase the number of samples.…”

Section: Introductionmentioning

confidence: 99%

Underwater target recognition methods based on the framework of deep learning: A survey

Teng

Zhao

2020

International Journal of Advanced Robotic Systems

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The accuracy of underwater target recognition by autonomous underwater vehicle (AUV) is a powerful guarantee for underwater detection, rescue, and security. Recently, deep learning has made significant improvements in digital image processing for target recognition and classification, which makes the underwater target recognition study becoming a hot research field. This article systematically describes the application of deep learning in underwater image analysis in the past few years and briefly expounds the basic principles of various underwater target recognition methods. Meanwhile, the applicable conditions, pros and cons of various methods are pointed out. The technical problems of AUV underwater dangerous target recognition methods are analyzed, and corresponding solutions are given. At the same time, we prospect the future development trend of AUV underwater target recognition.

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Classification of anti-submarine warfare sonar targets using a deep neural network

Cited by 22 publications

References 8 publications

A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

Detecting Submerged Objects Using Active Acoustics and Deep Neural Networks: A Test Case for Pelagic Fish

Underwater target recognition methods based on the framework of deep learning: A survey

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