Abstract
There is a need for automatic bird identification system at offshore wind farms in Finland. The developed system should be able to operate from onshore, which is cost-effective in terms of installations and maintenance. Indubitably, a radar is the obvious choice to detect flying birds, but external information is required for actual identification. A conceivable method is to exploit visual camera images. In the proposed system, the radar detects birds and provides the coordinates to camera steering system. The camera steering system tracks the flying birds, thus enabling capturing a series of images. Classification is based on the images, and it is implemented by a small convolutional neural network trained with a deep learning algorithm. We also propose a data augmentation method in which images are rotated and converted in accordance with the desired color temperatures. The final identification is based on a fusion of data provided by the radar and image data. We present the results of the number of correctly identified species based on manually taken images.
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References
Baxter, A.T., Robinson, A.P.: A comparison of scavenging bird deterrence techniques at UK landfill sites. Int. J. Pest Manag. 53, pp. 347–356 (2007). Taylor & Francis Online. https://doi.org/10.1080/09670870701421444
pelco-D protocol. Bruxy REGNET. http://bruxy.regnet.cz/programming/rs485/pelco-d.pdf
All About the Peregrine Falcon. U.S. Fish and Wildlife Service (1999). https://web.archive.org/web/20080416195055/http://www.fws.gov/endangered/recovery/peregrine/QandA.html#fast
Statistics. Finnish Meteorological Institute. http://ilmatieteenlaitos.fi/tuulitilastot
Robin Radar Models. Robin Radar Systems B.V. https://www.robinradar.com/
Richards, M.A.: Fundamentals of Radar Signal Processing. The McGraw-Hill Companies, New York (2005). ISBN: 0-07-144474-2
Bruderer, B.: The Study of Bird Migration by Radar, Part1: The Technical Basis. Naturwissenschaften, vol. 84, pp. 1–8. Springer-Verlag, Heidelberg (1997)
Fuzzy Logic Toolbox Documentation. The MathWorks Inc. https://se.mathworks.com/help/fuzzy/fuzzy.pdf
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975). Elsevier
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, pp. 330–371. MIT Press, Cambridge (2016). www.deeplearningbook.org
Wang, J., Perez, L.: The Effectiveness of Data Augmentation in Image Classification Using Deep Learning. Stanford University, Stanford (2017). http://cs231n.stanford.edu/reports/2017/pdfs/300.pdf
Speranskaya, N.I.: Determination of spectrum color co-ordinates for twenty-seven normal observers. Opt. Spectrosc. 7, 424–428 (1959). Springer
Stiles, W.S., Burch, J.M.: NPL colour-matching investigation: Final report. Opt. Acta 6, 1–26 (1959). Taylor & Francis
Wyszecki, G., Stiles, W.S.: Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd edn. Wiley, New York (1982)
Stockman, A., Sharpe, L.T.: Spectral sensitivities of the middle- and long-wavelength sensitive cones derived from measurements in observers of known genotype. Vis. Res. 40, 1711–1737 (2000). Elsevier
CIE Proceedings, Vienna Session 1963. Committee Report E-1.4.1, vol. B, pp. 209–220. Bureau Central de la CIE, Paris (1964)
Blackbody Color Datafile.: Vendian.org. http://www.vendian.org/mncharity/dir3/blackbody/UnstableURLs/bbr_color.html
Jarrett, K., Kavukcuoglu, K., Ranzato, M.A., LeCun, Y.: What is the best multi-stage architecture for object recognition. In: International Conference on Computer Vision, pp. 2146–2153. IEEE, Kyoto, Japan (2009)
Niemi, J., Tanttu, J.T.: Automatic bird identification for offshore Wind farms: a case study for deep learning. In: Proceedings of ELMAR-2017, 59th IEEE International Symposium ELMAR-2017, Croatian Society Electronics in Marine (2017). ISBN:978-953-184-230-3
Huang, J.F., LeCun, Y.: Large-scale learning with SVM and convolutional nets for generic object categorization. In: Computer Vision and Pattern Recognition Conference (CVPR06). IEEE Press, New York, NY (2006)
Desholm, M., Kahlert, J.: Avian collision risk at an offshore wind farm. Biol. Lett. 1, 296–298 (2005). The Royal Society Publishing https://doi.org/10.1098/rsbl.2005.0336
Marques, A.T., et al.: Understanding bird collisions at wind farms: an updated review on the causes and possible mitigation strategies. Biol. Conserv. 179, 40–52 (2014). Elsevier
Verhoef, J.P., Westra, C.A., Korterink, H., Curvers, A.: WT-Bird A Novel Bird Impact Detection System. ECN Research centre of the Netherlands (2002). https://www.ecn.nl/docs/library/report/2002/rx02055.pdf
Wiggelinkhuizen, E.J., Barhorst, S.A.M., Rademakers, L.W.M.M., den Boon, H.J.: Bird Collision Monitoring System for Multi-Megawatt Wind Turbines, WT-Bird: Prototype Development and Testing. ECN Research Centre of the Netherlands (2006). https://www.ecn.nl/publications/PdfFetch.aspx?nr=ECN-E--06-027
Wiggelinkhuizen, E.J., den Boon, H.J.: Monitoring of Bird Collisions in Wind Farm Under Offshore-Like Conditions Using WT-BIRD System: Final Report. ECN Research Centre of the Netherlands (2009). https://www.ecn.nl/docs/library/report/2009/e09033.pdf
DTBird.: Liquen Consultora Ambiental, S.L. http://www.dtbird.com/
MUSE.: DHI. https://www.dhigroup.com/global/news/2017/02/automated-bird-monitoring-system-lands-on-pioneer-us-wind-farm
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The authors wish to thank Suomen Hyötytuuli for the financial support and Robin Radar Systems for the technical support with the applied radar system.
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Niemi, J., Tanttu, J.T. (2019). Automatic Bird Identification for Offshore Wind Farms. In: Bispo, R., Bernardino, J., Coelho, H., Lino Costa, J. (eds) Wind Energy and Wildlife Impacts . Springer, Cham. https://doi.org/10.1007/978-3-030-05520-2_9
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