Skip to main content

A Detection System for Vertical Slot Fishways Using Laser Technology and Computer Vision Techniques

  • Conference paper
  • First Online:
Advances in Computational Intelligence (IWANN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9094))

Included in the following conference series:

Abstract

Vertical slot fishways are structures that are placed in rivers to allow fish to avoid obstacles such as dams, hydroelectric plants. Knowing the frequency with which fish go through this type of structures can help to determine their efficiency, as well as know migratory features from species, determine if the fluvial course is healthy or if it is possible to fish with fauna preservation guarantees.

A non-invasive method for fish detection, without the need of direct observation, which uses a laser sensor and computer vision techniques to detect fish, is proposed in this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Wu, S., Rajaratma, N., Katopodis, C.: Structure of flow in vertical slot fishways. Journal of Hydraulic Engineering 125, 351–360 (1999)

    Article  Google Scholar 

  2. Puertas, J., Pena, L., Teijeiro, T.: An Experimental Approach to the Hydraulics of Vertical Slot Fishways. Journal of Hydraulics Engineering 130 (2004)

    Google Scholar 

  3. Tarrade, L., Texier, A., David, L., Larinier, M.: Topologies and measurements of turbulent flow in vertical slot fishways. Hydrobiologia 609, 177–188 (2008)

    Article  Google Scholar 

  4. Dewar, H., Graham, J.: Studies of tropical tuna swimming performance in a large water tunnel– Energetics. Journal of Experimental Biology 192, 13–31 (1994)

    Google Scholar 

  5. Blake, R.W.: Fish functional design and swimming performance. Journal of fish biology 65, 1193–1222 (2004)

    Article  Google Scholar 

  6. Rodriguez, A., Bermudez, M., Rabuñal, J., Puertas, J., Dorado, J., Balairon, L.: Optical Fish Trajectory Measurement in Fishways through Computer Vision and Artificial Neural Networks. Journal of Computing in Civil Engineering 25, 291–301 (2011)

    Article  Google Scholar 

  7. Puertas, J., Cea, L., Bermudez, M., Pena, L., Rodriguez, A., Rabuñal, J., et al.: Computer application for the analysis and design of vertical slot fishways in accordance with the requirements of the target species. Ecological Engineering 48, 51–60 (2012)

    Article  Google Scholar 

  8. Craig, R.E., Forbes, S.T.: Design of a sonar for fish counting (1969)

    Google Scholar 

  9. Ehrenberg, J.E.: A method for extracting the fish target strength distribution from acoustic echoes. In: Ocean 72-IEEE International Conference on Engineering in the Ocean Environment, pp. 61–64 (1972)

    Google Scholar 

  10. Balk, H., Lindem, T.: Improved fish detection in data from split-beam sonar. Aquatic Living Resources 13, 297–303 (2000)

    Article  Google Scholar 

  11. Belcher, E., Matsuyama, B., Trimble, G.: Object identification with acoustic lenses. In: MTS/IEEE Conference and Exhibition OCEANS, pp. 6–11 (2001)

    Google Scholar 

  12. Han, J., Honda, N., Asada, A., Shibata, K.: Automated acoustic method for counting and sizing farmed fish during transfer using DIDSON. Fisheries Science 75, 1359–1367 (2009)

    Article  Google Scholar 

  13. Holmes, J.A., Cronkite, G.M., Enzenhofer, H.J., Mulligan, T.J.: Accuracy and precision of fish-count data from a “dual-frequency identification sonar”(DIDSON) imaging system. ICES Journal of Marine Science: Journal Du Conseil 63, 543–555 (2006)

    Article  Google Scholar 

  14. Mitra, V., Wang, C.-J., Banerjee, S.: Lidar detection of underwater objects using a neuro-SVM-based architecture. IEEE Transactions on Neural Networks 17, 717–731 (2006)

    Article  Google Scholar 

  15. Baumgartner, L., Bettanin, M., McPherson, J., Jones, M., Zampatti, B., Beyer, K.: Assessment of an infrared fish counter (Vaki Riverwatcher) to quantify fish migrations in the Murray-Darling Basin. Industry & Investment NSW, Fisheries Final Report Series 116, 47 (2010)

    Google Scholar 

  16. White, D., Svellingen, C., Strachan, N.: Automated measurement of species and length of fish by computer vision. Fisheries Research 80, 203–210 (2006)

    Article  Google Scholar 

  17. Zion, B., Alchanatis, V., Ostrovsky, V., Barki, A., Karplus, I.: Real-time underwater sorting of edible fish species. Computers and Electronics in Agriculture 56, 34–45 (2007)

    Article  Google Scholar 

  18. Storbeck, F., Daan, B.: Fish species recognition using computer vision and a neural network. Fisheries Research 51, 11–15 (2001)

    Article  Google Scholar 

  19. Coifman, B., Beymer, D., McLauchlan, P., Malik, J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C: Emerging Technologies 6, 271–288 (1998)

    Article  Google Scholar 

  20. Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: IEEE ICCV, pp. 1–19 (1999)

    Google Scholar 

  21. KaewTraKulPong, P., Bowden, R., An improved adaptive background mixture model for real-time tracking with shadow detection. In: Video-based surveillance systems, pp. 135–144. Springer (2002)

    Google Scholar 

  22. Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, pp. 28–31 (2004)

    Google Scholar 

  23. Godbehere, A.B., Matsukawa, A., Goldberg, K.: Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. American Control Conference (ACC) 2012, 4305–4312 (2012)

    Google Scholar 

  24. Haralick, R.M., Sternberg, S.R., Zhuang, X.: Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 532–550 (1987)

    Google Scholar 

  25. Morales R.R., Azuela, J.H.S., Procesamiento y análisis digital de imágenes. Ra-Ma (2011)

    Google Scholar 

  26. Ochoa Somuanom, J., Pérez Lara, C., Toscano Martínez, J.H., Pereyra Ramos, C.G.: Clasificación de objetos rígidos a partir de imágenes digitales empleando los momentos invariantes de Hu. Presented at the X Congreso Internacional Sobre Innovación y Desarrollo Tecnológico, Cuernavaca Morelos, México (2013)

    Google Scholar 

  27. Pajares Martinsanz, G., De la Cruz García, J.: Visión por computador imágenes digitales y aplicaciones, 2ª edn. Ra-Ma (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Angel J. Rico-Diaz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Rico-Diaz, A.J., Rodriguez, A., Villares, D., Rabuñal, J.R., Puertas, J., Pena, L. (2015). A Detection System for Vertical Slot Fishways Using Laser Technology and Computer Vision Techniques. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19258-1_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19257-4

  • Online ISBN: 978-3-319-19258-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics