Creative Commons "Este é um artigo publicado em acesso aberto sob uma licença Creative commons (CC BY 4.0). Fonte: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125996889&origin=inward. Acesso em: 22 agosto de 2022.DOMINGOS, L. C. F.Paulo SantosSKELTON, P. S. M.BRINKWORTH, R. S. A.SAMMUT, K.2022-04-012022-04-012022-03-02DOMINGOS, L. C. F.; SANTOS, P.; SKELTON, P. S. M.; BRINKWORTH, R. S. A.; SAMMUT, K. A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance. Sensors, v. 22, n. 6. March, 2022.1424-8220https://repositorio.fei.edu.br/handle/FEI/4455© 2022 by the authors. Licensee MDPI, Basel, Switzerland.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.Acesso AbertoArtigo de revisão10.3390/s22062181Deep convolutional neural networksObjects’ classificationUnderwater acoustics