Sequential Fish Catch Counter Using Vision-based Fish Detection and Tracking

Riko Tanaka, Teppei Nakano, Tetsuji Ogawa

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


An attempt has been made to develop a system for sequentially counting the number of fish caught using images taken on board. Fish catch counting for each local sea area contributes to fishery resource management and decision support for efficient operation. In this case, visual information is helpful for an intuitive explanation. The developed system consists of fish detection, fish tracking, and overdetected track deletion: to count fish robustly to its movement around on a deck, the fish detection stage attempts to absorb changes in the appearance of the fish, while the tracking stage dares not to use the appearance information to prevent the tracks from being unduly disconnected. Experimental comparisons using onboard video data of bullet tuna trolling demonstrated that the system could count fish with 89% precision and 87% recall.

Original languageEnglish
JournalOceans Conference Record (IEEE)
Publication statusPublished - 2022
EventOCEANS 2022 - Chennai - Chennai, India
Duration: 2022 Feb 212022 Feb 24


  • bullet tuna trolling
  • Deep neural networks
  • fish catch counting
  • object detection
  • object tracking

ASJC Scopus subject areas

  • Oceanography


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