Automatic Detection of Underwater Objects in Sonar Imagery

Avi Abu, Roee DIamant

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper introduces a new unsupervised statistically-based algorithm for the detection of underwater objects in sonar imagery. Highlights are detected by a higher-order-statistics representation of the image followed by a segmentation process to form a region-of-interest (ROI). Our algorithm sets its main parameters in situ and avoids the need of parameter calibration. Moreover, we do not require knowledge about the target's shape or size, thereby making our algorithm robust to any sonar detection application. Results obtained from real sonar system show a good trade-off between probability of detection and false alarm rate (FAR).

Original languageEnglish
Title of host publicationOCEANS 2019 - Marseille, OCEANS Marseille 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728114507
DOIs
StatePublished - Jun 2019
Event2019 OCEANS - Marseille, OCEANS Marseille 2019 - Marseille, France
Duration: 17 Jun 201920 Jun 2019

Publication series

NameOCEANS 2019 - Marseille, OCEANS Marseille 2019
Volume2019-June

Conference

Conference2019 OCEANS - Marseille, OCEANS Marseille 2019
Country/TerritoryFrance
CityMarseille
Period17/06/1920/06/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Sonar image processing
  • binary hypothesis testing
  • detection in sonar imagery
  • highlight detection
  • image segmentation
  • likelihood ratio test

ASJC Scopus subject areas

  • Oceanography
  • Automotive Engineering
  • Management, Monitoring, Policy and Law
  • Water Science and Technology
  • Instrumentation

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