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

doi:10.1016/S1672-6529(08)60120-1

Abstract

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

Yeffry Handoko, Yul.Y. Nazaruddin, Huosheng Hu. Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types[J].J4, 2009, 6(3): 264-269.

References

[1] Fish Finder,
[2009-04-16], http://www.fish-finder.org

[2] Yu J Z, Chen E K, Wang S, Tan M. Motion control algorithms for a free-swimming biomimetic robot fish. Acta Automatica Sinica, 2005, 31, 537–542.

[3] Brehmer P, Lafont T, Georgakarakos S, Josse E, Gerlotto F, Collet C. Omnidirectional multibeam sonar monitoring: Applications in fisheries science. Fish and Fisheries, 2006, 7, 165–179.

[4] Brehmer P, Georgakarakos S, Josse E, Trygonis V, Dalen J. Adaptation of fisheries sonar for pelagic fish school monitoring: dependency of the schooling behaviour on the fish finding efficiency. Aquatic Living Resources, 2007, 20, 377–384.

[5] Handoko Y, Nazaruddin Y Y, Riyanto B, Leksono E. Implementing artificial catching-prey behavior using fuzzy logic on fish robot. Proceeding of The 9th Conference in Instrumentation and Control, Bandung Institute of Technology, Indonesia, 2007, 1–4.

[6] Lee K C, Ou J S, Huang M C, Fang M C. A novel location estimation based on pattern matching algorithm in underwater environments. Applied Acoustics, 2009, 70, 479–483.

[7] Tidd R A, Wilder J. Fish detection and classification system. Journal of Electronic Imaging, 2001, 10, 283–288.

[8] Estévez P B, Ruz G A, Perez C A. Fuzzy min-max neural network for image segmentation. The 7th Joint Conference on Information Science, North Carolina, USA, 2003, 26–30.

[9] Kulinchenko A B, Simpson P K, Denny G F. Tethered fish data collection and species classification: Prince William Sound bottomfish. Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2004, 2, 1439– 1443.

[10] Hu B G, Gosine R G, Cao L X, de Silva C W. Application of fuzzy classification technique in computer grading of fish products. IEEE Transactions on Fuzzy Systems, 1998, 6, 144–152.

[11] MaxSonar,
[2009-04-16], http://www.maxbotix.com/

Sfakiotakis M, Lane D M, Davies J B. Review of fish swimming modes for aquatic locomotion. IEEE Journal of Oceanic Engineering, 1999, 24, 237–252.

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