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Deep-Sea Organisms Tracking Using Dehazing and Deep Learning

  • Huimin Lu
  • , Tomoki Uemura
  • , Dong Wang
  • , Jihua Zhu
  • , Zi Huang
  • , Hyoungseop Kim

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

Deep-sea organism automatic tracking has rarely been studied because of a lack of training data. However, it is extremely important for underwater robots to recognize and to predict the behavior of organisms. In this paper, we first develop a method for underwater real-time recognition and tracking of multi-objects, which we call “You Only Look Once: YOLO”. This method provides us with a very fast and accurate tracker. At first, we remove the haze, which is caused by the turbidity of the water from a captured image. After that, we apply YOLO to allow recognition and tracking of marine organisms, which include shrimp, squid, crab and shark. The experiments demonstrate that our developed system shows satisfactory performance.

Original languageEnglish
Pages (from-to)1008-1015
Number of pages8
JournalMobile Networks and Applications
Volume25
Issue number3
DOIs
StatePublished - 1 Jun 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 14 - Life Below Water
    SDG 14 Life Below Water

Keywords

  • Dehazing
  • Organisms tracking
  • YOLO

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