DOI QR코드

DOI QR Code

Measurement of size and swimming speed of Bluefin tuna (Thunnus thynnus) using by a stereo vision method

스테레오 카메라 기법을 이용한 참다랑어의 크기 및 유영속도 측정

  • Yang, Yong-Su (Fisheries System Engineering Division, National Fisheries Research & Development Institute) ;
  • Lee, Kyoung-Hoon (Fisheries System Engineering Division, National Fisheries Research & Development Institute) ;
  • Ji, Seong-Chul (Future Aquaculture Research Center, National Fisheries Research & Development Institute) ;
  • Jeong, Seong-Jae (Aquaculture Industry Division, East Sea Fisheries Research Institute) ;
  • Kim, Kyong-Min (Future Aquaculture Research Center, National Fisheries Research & Development Institute) ;
  • Park, Seong-Wook (Fisheries System Engineering Division, National Fisheries Research & Development Institute)
  • 양용수 (국립수산과학원 시스템공학과) ;
  • 이경훈 (국립수산과학원 시스템공학과) ;
  • 지승철 (국립수산과학원 미래양식연구센터) ;
  • 정성재 (동해수산연구소 해역산업과) ;
  • 김경민 (국립수산과학원 미래양식연구센터) ;
  • 박성욱 (국립수산과학원 시스템공학과)
  • Received : 2011.05.25
  • Accepted : 2011.08.12
  • Published : 2011.08.31

Abstract

This study was performed to develop a video based system which can be used to measure the averaged fish size in a non-intrusive fashion. The design was based on principles of simple stereo geometry, incorporated fish dimensions weight relationships and took into consideration fish movement to lower system costs. As the fish size is an important factor that impacts the economy of an aquaculture enterprise. Size measurements, including fork length, width or height, girth, thickness and mass, can be used to determine fish condition in the fish farm, so the averaged fish size of fish cage needs to consistently monitor in open ocean aquaculture cage. A precision of ${\pm}3%$ for replicate length measurements of a 60cm bar is obtained at distances between 2.0 and 6.0m, and the mean fork length and mean swimming speed of bluefin tuna were estimated to 48.8cm and 0.78FL/s, respectively.

Acknowledgement

Supported by : 국립수산과학원

References

  1. An, H.C., K.H. Lee, J.H. Bae, B.S. Bae and J.K. Shin, 2009. Estimation of the distribution density of snow crab, Chionoecetes opilio using a deep-sea underwater camera system attached on a towing sledge. J. Kor. Soc. Fish. Tech., 45 (3), 151-156. https://doi.org/10.3796/KSFT.2009.45.3.151
  2. Costa, C., A. Loy, S. Cataudella, D. Davis and M. Scardi, 2006. Extracting fish size using dual underwater cameras. aquacultural engineering, 35, 218-227. https://doi.org/10.1016/j.aquaeng.2006.02.003
  3. Davis, C.S., S.M. Gallager and A.R. Solow, 1992. Microaggregations of oceanic plankton observed by towed video microscopy. Science, 257, 230-232. https://doi.org/10.1126/science.257.5067.230
  4. Doh, D.H., D.H. Kim, K.R. Cho, Y.B. Cho, T. Saga and T. Kobayashi, 2002. Development of GA based 3D-PTV technique. Journal of Visualization, 5 (3), 243-254. https://doi.org/10.1007/BF03182332
  5. Harvey, E., M. Cappo, M. Shortis, S. Robson, J. Buchanan and P. Speare, 2003. The accuracy and precision of underwater measurements of length and maximum body depth of southern bluefin tuna (Thunnus maccoyii) with a stereo-video camera system. Fish. Res., 63, 315-326. https://doi.org/10.1016/S0165-7836(03)00080-8
  6. Holmes, J.A., G.M.W. Cronkite, H.J. Enzenhofer and T.J. Mulligan, 2006. Accuracy and precision of fishcount data from a dual-frequency identification sonar (DIDSON) imaging system. ICES Journal of Marine Science, 63, 543-555. https://doi.org/10.1016/j.icesjms.2005.08.015
  7. Kim, M.Y. and Y.H. Lee, 2001. Development of highresolution 3-D PIV Algorithm by Cross-correlation. Pro. of the KSME Fall Annual Meeting B, 410-416.
  8. Moursund, R.A., T.J. Carlson and R.D. Peters, 2003. A fisheries applications of a dual frequency identification sonar acoustic camera. ICES Journal of Marine Science, 60, 678-683. https://doi.org/10.1016/S1054-3139(03)00036-5
  9. Okamoto, K., S. Nishio, T. Kobayashi and T. Saga, 1997. Standard Images for particle Image velocimetry. Proc. PIV'97-Fukui, 229-236.
  10. Pasad, A.K. and R.J. Adrian, 1993. Stereoscopic particle image velocimetry applied to liquid flows. Exp.Fluids., 15, 49-60.
  11. Schenk, T. and C.K. Toth, 1992. Computer Vision and Digital Photo-grammetry. ITC Journal, 24-38.

Cited by

  1. A Study on System for measuring morphometric characteristis of fish using morphological image processing vol.48, pp.4, 2012, https://doi.org/10.3796/KSFT.2012.48.4.469
  2. Development of a vaccine automation injection system for flatfish using a template matching vol.48, pp.2, 2012, https://doi.org/10.3796/KSFT.2012.48.2.165
  3. A study on structural feature and size distribution of swimming fish using an 3 dimensional pattern laser vol.52, pp.2, 2016, https://doi.org/10.3796/KSFT.2016.52.2.103