On the Use of Tiny Convolutional Neural Networks for Human-Expert-Level Classification Performance in Sonar Imagery

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46 Scopus citations

Abstract

Efficient convolutional neural networks (CNNs) are designed and trained for an underwater target classification task with synthetic aperture sonar (SAS) imagery collected at sea. The main contribution is demonstrating that classification performance that matches, and even surpasses, the level achievable by a human domain expert obtained from tiny CNNs with three to six orders of magnitude fewer parameters than have traditionally been used in the literature. In doing so, this work represents the first large-scale classification study in the sonar domain to establish a favorable comparison between automated algorithm performance and human ability. Extensive experimental results on challenging real-world SAS image data sets collected in diverse environments and conditions demonstrate that the CNNs possess strong generalization ability. These findings should significantly impact the manner in which CNNs are utilized in the underwater remote-sensing community. To wit, the tiny CNNs proposed here provide a blueprint for achieving excellent classification performance even with limited computing power or limited data.

Original languageEnglish (US)
Article number8984248
Pages (from-to)236-260
Number of pages25
JournalIEEE Journal of Oceanic Engineering
Volume46
Issue number1
DOIs
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Ocean Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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