A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance.
Average rating votes
Domingos, Lucas C. F.
Santos, Paulo E.
Skelton, Phillip S. M.
Brinkworth, Russell S. A.
MetadataShow full item record
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 deeplearning 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.....
Sustainable Development Goals (SDG)14.a
CitationDomingos, L.C.F.; Santos, P.E.; Skelton, P.S.M.; Brinkworth, R.S.A.; Sammut, K. (2022) A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance. Sensors , 22: 2181, 30pp. DOI: https:// doi.org/10.3390/s22062181
The following license files are associated with this item: