Standardization of Sonar Images by Conditional Random Fields for Fish Segmentation with Mask R-CNN

Chin Chun Chang, Yen Po Wang, Shyi Chyi Cheng

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

2 Scopus citations

Abstract

Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. A preprocessing convolutional neural network (PreCNN) is proposed to provide "standardized"feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective.

Original languageEnglish
Title of host publicationISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems
Subtitle of host publication5G Dream to Reality, Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665419512
DOIs
StatePublished - 2021
Event2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021 - Hualien, Taiwan
Duration: 16 Nov 202119 Nov 2021

Publication series

NameISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding

Conference

Conference2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021
Country/TerritoryTaiwan
CityHualien
Period16/11/2119/11/21

Keywords

  • Conditional Random Fields
  • Fish Segmentation
  • Mask R-CNN
  • Sonar Images

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