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A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance
2022
Sensors
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
doi:10.3390/s22062181
pmid:35336352
pmcid:PMC8954367
fatcat:t4ol7zpkbrbujez2teg3vg7sge