Abstract
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.
Highlights
The importance of developing accurate automatic object classification methods for underwater sensor data in general, and Sound Navigation and Ranging data in particular, is directly related to the variety of potential applications depending on them.Examples of marine classification tasks include the inspection of underwater structures for the offshore industry [1,2], the identification of underwater archaeological remains [3], the surveillance of shorelines [4], counting and classifying the behaviour of marine life for biological research [5] and the identification of vessels [6] to cite a few
Results show that ImageNet can be used effectively by a CNN to extract general features that can provide better accuracy when training a sonar classifier with small datasets
A review of the literature related to the application of deep-learning methods for automatic object detection and classification of underwater acoustic data was presented in this paper
Summary
The importance of developing accurate automatic object classification methods for underwater sensor data in general, and Sound Navigation and Ranging (sonar) data in particular, is directly related to the variety of potential applications depending on them.Examples of marine classification tasks include the inspection of underwater structures for the offshore industry [1,2], the identification of underwater archaeological remains [3], the surveillance of shorelines [4], counting and classifying the behaviour of marine life for biological research [5] and the identification of vessels [6] to cite a few. We present a summary of underwater acoustics, classic signal processing methods and general deep-learning algorithms These constitute the background knowledge needed to understand and analyse the current methods for the autonomous classification of underwater sonar data in the maritime domain. The automatic classification of this type of signal is a challenging task, as the signal is dependent on the vessel’s speed, the age and state of the propulsion system, the highly variable background noise and the diversity of sound propagation mechanisms in the ocean. The latter aspect is a source of complexity in active sonar applications. In order to provide an appropriate context for this issue, this section presents a brief summary of underwater acoustics, mainly based on [20–22]
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