ReviewSensors (Basel, Switzerland)2022
A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- A lightweight hybrid attention network with multi-scale feature integration for intelligent recognition of underwater acoustic targets.Scientific reports · 2026Article
- An efficient deep learning approach with frequency and channel optimization for underwater acoustic target recognition.Scientific reports · 2025Article
- Innovative Regression Model for Frequency-Dependent Acoustic Source Strength in the Aquatic Environment: Bridging Scientific Insight and Practical Applications.Sensors (Basel, Switzerland) · 2025Article
- Article
- Few-shot learning for joint model in underwater acoustic target recognition.Scientific reports · 2023Article
- An Efficient Compression Method of Underwater Acoustic Sensor Signals for Underwater Surveillance.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.