J4 ›› 2009, Vol. 6 ›› Issue (3): 264-269.doi: 10.1016/S1672-6529(08)60120-1

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Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types

Yeffry Handoko1, Yul.Y. Nazaruddin1, Huosheng Hu2   

  1. 1. Department of Engineering Physics, Bandung Institute of Technology, Jl. Ganesa 10 Bandung 40132, Indonesia
    2. School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
  • 出版日期:2009-09-30
  • 通讯作者: Huosheng Hu E-mail: hhu@essex.ac.uk E-mail:hhu@essex.ac.uk

Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types

Yeffry Handoko1, Yul.Y. Nazaruddin1, Huosheng Hu2   

  1. 1. Department of Engineering Physics, Bandung Institute of Technology, Jl. Ganesa 10 Bandung 40132, Indonesia
    2. School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
  • Online:2009-09-30
  • Contact: Huosheng Hu E-mail: hhu@essex.ac.uk E-mail:hhu@essex.ac.uk

摘要:

Fish finders have already been widely available in the fishing market for a number of years. However, the sizes of these fish finders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine. The goal of this research is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor. With some pre-condition in hardware and algorithms, the experimental results show that the proposed design has good per-formance, with a detection rate of 100 % and a classification rate of 94 %. Both the existing type of fish and the group behavior can be revealed by statistical interpretations such as hovering passion and sparse swimming mode.

关键词: fish detection, classification, artificial neural network, ultrasound sensor

Abstract:

Fish finders have already been widely available in the fishing market for a number of years. However, the sizes of these fish finders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine. The goal of this research is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor. With some pre-condition in hardware and algorithms, the experimental results show that the proposed design has good per-formance, with a detection rate of 100 % and a classification rate of 94 %. Both the existing type of fish and the group behavior can be revealed by statistical interpretations such as hovering passion and sparse swimming mode.

Key words: fish detection, classification, artificial neural network, ultrasound sensor