A fast sonar-based benthic object recognition model via extreme learning machine

Wenqiang Cai, Rui Nian, Bo He, Amaury Lendasse

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

2 Citations (Scopus)

Abstract

The fast sonar-based object recognition turns out to be one of the most challenging topics in the underwater signal analysis. In this paper, we try to develop a fast benthic object recognition model via the extreme learning machine (ELM) on the basis of the structured geometrical feature extraction. Geometrical features such as major and minor axis, eccentricity, circularity and so on are employed to construct learning samples of ELM. The classifier based on ELM is used to recognize the target objects in sonar images. It has been shown in the simulation experiments that the proposed model could keep a quite good recognition performance with a much fast speed.

Original languageEnglish
Title of host publicationOCEANS'15 MTS/IEEE Washington
PublisherIEEE
Number of pages4
ISBN (Electronic)978-0-9339-5743-5
Publication statusPublished - 8 Feb 2016
MoE publication typeA4 Conference publication
EventOCEANS - Washington, United States
Duration: 19 Oct 201522 Oct 2015
https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=20940

Conference

ConferenceOCEANS
Country/TerritoryUnited States
CityWashington
Period19/10/201522/10/2015
Internet address

Keywords

  • ELM
  • Geometrical Feature Extraction
  • Object Recognition
  • Sonar Image

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