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Development of a vaccine automation injection system for flatfish using a template matching

템플릿 매칭을 이용한 넙치용 백신자동접종시스템 개발

  • Lee, Dong-Gil (Fisheries System Engineering Division, National Fisheries Research & Development Institute) ;
  • Yang, Young-Su (Fisheries System Engineering Division, National Fisheries Research & Development Institute) ;
  • Park, Seong-Wook (Fisheries System Engineering Division, National Fisheries Research & Development Institute) ;
  • Cha, Bong-Jin (Fisheries System Engineering Division, National Fisheries Research & Development Institute) ;
  • Xu, Guo-Cheng (Department of Electrical Engineering, Pusan National University) ;
  • Kim, Jong-Rak (Department of Facilities management, Yeongnam Headquarters, Korea Rail Network Authority)
  • 이동길 (국립수산과학원 시스템공학과) ;
  • 양용수 (국립수산과학원 시스템공학과) ;
  • 박성욱 (국립수산과학원 시스템공학과) ;
  • 차봉진 (국립수산과학원 시스템공학과) ;
  • 허국성 (부산대학교 전자전기공학과) ;
  • 김종락 (한국철도시설공단 영남본부 시설관리부)
  • Received : 2012.02.29
  • Accepted : 2012.05.09
  • Published : 2012.05.31

Abstract

Nationally, flatfish vaccination has been performed manually, and is a laborious and time-consuming procedure with low accuracy. The handling requirement also makes it prone to contamination. With a view to eliminating these drawbacks, we designed an automatic vaccine system in which the injection is delivered by a Cartesian coordinate robot guided by a vision system. The automatic vaccine injection system is driven by an injection site location algorithm that uses a template-matching technique. The proposed algorithm was designed to derive the time and possible angles of injection by comparing a search area with a template. The algorithm is able to vaccinate various sizes of flatfish, even when they are loaded at different angles. We validated the performance of the proposed algorithm by analyzing the injection error under randomly generated loading angles. The proposed algorithm allowed an injection rate of 2000 per hour on average. Vaccination of flatfish with a body length of up to 500mm was possible, even when the orientation of the fish was random. The injection errors in various sizes of flatfish were very small, ranging from 0 to 0.6mm.

Keywords

Vision system;Cartesian coordinate robot;Flatfish;Template matching

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. Darwish, A.M and A.K Jain, 1988. A rule based approach for visual pattern inspection. IEEE Trans. of Pattern Analysis and Machine. 10 (1), 56-68. https://doi.org/10.1109/34.3867
  3. Di Stefano, L. S. Nattoccia, and F. Tombari, 2005. Speeding-up NCC-based template matching using parallel multimedia instructions. The 7th IEEE International Workshop on 4-6 July 2005, 193- 197.
  4. Kim, P. and S. Rhee., 1999. Three-dimensional inspection of ball grid array using laser vision system. IEEE Trans. of Manufacturing Technology, 22 (2), 151-155.
  5. Kwon M.G and J.D. Bang, 2004. Effects of immersion vaccination in different concentration of edwardsiellosis vaccin on olive flounder, Paralichthys olivaceus. J. Fish Pathol., 17 (3), 171-177.
  6. Lee, D.J., 2011. Performance characteristics of a multidirectional underwater CCTV camera system to use in the artificial reef survey. J. Kor. Soc. Fish. Tech., 47 (2), 146-152. https://doi.org/10.3796/KSFT.2011.47.2.146
  7. National Oceanic and Atmospheric Administration Fisheries: Office of Science and Technology. 2008. Retrieved from http://www.st.nmfs.noaa.gov/st1/fus/fus08/ index.html (accessed 11.10.10).
  8. Storbeck, F. and B. Daan, 2001. Fish species recognition using computer vision and a neural network. Fish. Res., 51 (1), 11-15. https://doi.org/10.1016/S0165-7836(00)00254-X
  9. Sun, Y. and B.J. Nelson, 2002. Biological cell injection using an autonomous microrobotic system. Int. J. Robot. Res., 21 (10-11), 861-868. https://doi.org/10.1177/0278364902021010833
  10. Tsai, D.M. and C.T. Lin, 2003. Fast normalized cross correlation for defect detection. Pattern Recognition Letters, 24 (12), 2625-2631. https://doi.org/10.1016/S0167-8655(03)00106-5
  11. Yang, Y.S., K.H. Lee, S.C. Ji, S.J. Jeong, K.M. Kim, S.W. Park, 2011. Measurement of size and swimming speed of Bluefin tuna (Thunnus thynnus) using by a stereo vision method. J. Kor. Soc. Fish. Tech., 47 (3), 214-221. https://doi.org/10.3796/KSFT.2011.47.3.214

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