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Hammerhead Shark Species Monitoring with Deep Learning

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Applications of Computational Intelligence (ColCACI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1346))

Abstract

In this paper, we propose a new automated method based on deep convolutional neural networks to detect and track critically endangered hammerhead sharks in video sequences. The proposed method improved the standard YOLOv3 deep architecture by adding 18 more layers (16 convolutional and 2 Yolo layers), which increased the model performance in detecting the species under analysis at different scales. According to the frame analysis based validation, the proposed method outperformed the standard YOLOv3 model and was similar to the mask R-CNN model in terms of accuracy scores for the majority of inspected frames. Also, the mean of precision and recall on an experimental frames dataset formed using the 10-fold cross-validation method highlighted that the proposed method outperformed the remaining architectures, reaching scores of 0.99 and 0.93, respectively. Furthermore, the methods were able to avoid introducing false positive detection. However, they were unable to handle the problem of species occlusion. Our results indicate that the proposed method is a feasible alternative tool that could help to monitor relative abundance of hammerhead sharks in the wild.

Supported by Universidad San Francisco de Quito USFQ.

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References

  1. Bouchet, P.J., Meeuwig, J.J.: Drifting baited stereo-videography: a novel sampling tool for surveying pelagic wildlife in offshore marine reserves. Ecosphere 6(8), art137 (2015). https://doi.org/10.1890/ES14-00380.1. https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/ES14-00380.1

  2. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 120, 122–125 (2000)

    Google Scholar 

  3. Curilem, M., Canário, J.P., Franco, L., Rios, R.A.: Using CNN to classify spectrograms of seismic events from Llaima Volcano (Chile). In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2018). https://doi.org/10.1109/IJCNN.2018.8489285

  4. Dionisi, A., Sardini, E., Serpelloni, M.: Wearable object detection system for the blind. In: 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings, pp. 1255–1258. IEEE (2012)

    Google Scholar 

  5. Fields, A.T., Fischer, G.A., Shea, S.K.H., Zhang, H., Feldheim, K.A., Chapman, D.D.: DNA zip-coding: identifying the source populations supplying the international trade of a critically endangered coastal shark. Anim. Conserv. https://doi.org/10.1111/acv.12585. https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1111/acv.12585

  6. Garcia, R., et al.: Automatic segmentation of fish using deep learning with application to fish size measurement. ICES J. Mar. Sci. 77(4), 1354–1366 (2020)

    Article  Google Scholar 

  7. GNU Lesser General Public License (LGPL) version 2.1: Ffmpeg tools. https://www.ffmpeg.org/. Accessed 23 Mar 2020

  8. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  9. Hearn, A.R., Bucaram, S.J.: Ecuador’s sharks face threats from within. Science 358(6366), 1009 (2017)

    Article  Google Scholar 

  10. Hearn, A.R., et al.: Elasmobranchs of the Galapagos Marine Reserve. In: Denkinger, J., Vinueza, L. (eds.) The Galapagos Marine Reserve. SEIGI, pp. 23–59. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02769-2_2

    Chapter  Google Scholar 

  11. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  12. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  13. Maire, F., Alvarez, L.M., Hodgson, A.: Automating marine mammal detection in aerial images captured during wildlife surveys: a deep learning approach. In: Pfahringer, B., Renz, J. (eds.) AI 2015. LNCS (LNAI), vol. 9457, pp. 379–385. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26350-2_33

    Chapter  Google Scholar 

  14. Martinez-Ortiz, J., Aires-da Silva, A.M., Lennert-Cody, C.E., Maunder, M.N.: The Ecuadorian artisanal fishery for large pelagics: species composition and spatio-temporal dynamics. PLOS ONE 10 (2015)

    Google Scholar 

  15. Oliphant, T.: NumPy: A guide to NumPy. Trelgol Publishing, USA (2006). http://www.numpy.org/. Accessed \(<\)today\(>\)

  16. O’Mahony, N., et al.: Deep learning vs. traditional computer vision. In: Arai, K., Kapoor, S. (eds.) CVC 2019. AISC, vol. 943, pp. 128–144. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-17795-9_10

    Chapter  Google Scholar 

  17. Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8026–8037 (2019)

    Google Scholar 

  18. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  19. Peñaherrera-Palma, C., et al.: Evaluating abundance trends of iconic species using local ecological knowledge. Biol. Conserv. 225, 197–207 (2018)

    Article  Google Scholar 

  20. Purushotham, S., Tripathy, B.K.: Evaluation of classifier models using stratified tenfold cross validation techniques. In: Krishna, P.V., Babu, M.R., Ariwa, E. (eds.) ObCom 2011. CCIS, vol. 270, pp. 680–690. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29216-3_74

    Chapter  Google Scholar 

  21. Python Core Team: Python 3.6.9: A dynamic, open source programming language. Python Software Foundation (2019). https://www.python.org/

  22. Raghunandan, A., Raghav, P., Aradhya, H.R., et al.: Object detection algorithms for video surveillance applications. In: 2018 International Conference on Communication and Signal Processing (ICCSP), pp. 0563–0568. IEEE (2018)

    Google Scholar 

  23. Raza, K., Hong, S.: Fast and accurate fish detection design with improved YOLO-v3 model and transfer learning. Int. J. Adv. Comput. Sci. Appl. 11, 7–16 (2020)

    Google Scholar 

  24. Redmon, J.: Darknet: open source neural networks in C (2013–2016). http://pjreddie.com/darknet/

  25. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  26. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  27. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  28. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 91–99. Curran Associates, Inc. (2015)

    Google Scholar 

  29. Rigby, C., et al.: Sphyrna Lewini. The IUCN red list of threatened species 2019: e. t39385a2918526 (2019)

    Google Scholar 

  30. Sung, M., Yu, S., Girdhar, Y.: Vision based real-time fish detection using convolutional neural network. In: OCEANS 2017, Aberdeen. pp. 1–6 (2017)

    Google Scholar 

  31. Tseng, C.H., Kuo, Y.F.: Detecting and counting harvested fish and identifying fish types in electronic monitoring system videos using deep convolutional neural networks. ICES J. Mar. Sci. 77, 1367–1378 (2020)

    Article  Google Scholar 

  32. Uemura, T., Lu, H., Kim, H.: Marine organisms tracking and recognizing using YOLO. In: Lu, H., Yujie, L. (eds.) 2nd EAI International Conference on Robotic Sensor Networks. EICC, pp. 53–58. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-17763-8_6

    Chapter  Google Scholar 

  33. Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018 (2018)

    Google Scholar 

  34. van der Walt, S., et al.: The Scikit-image contributors: Scikit-image: image processing in Python. PeerJ 2, e453 (2014). https://doi.org/10.7717/peerj.453

    Article  Google Scholar 

  35. Wang, Q., Bi, S., Sun, M., Wang, Y., Wang, D., Yang, S.: Deep learning approach to peripheral leukocyte recognition. PLoS ONE 14(6) (2019)

    Google Scholar 

  36. Xu, L., Bennamoun, M., An, S., Sohel, F., Boussaid, F.: Deep learning for marine species recognition. In: Balas, V.E., Roy, S.S., Sharma, D., Samui, P. (eds.) Handbook of Deep Learning Applications. SIST, vol. 136, pp. 129–145. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11479-4_7

    Chapter  Google Scholar 

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Acknowledgment

The authors thank the Applied Signal Processing and Machine Learning Research Group USFQ for providing the computing infrastructure (NVidia DGX workstation) to implement and execute the developed source code. The hammerhead shark videos used in this study were provided by Jonathan R. Green, Chris Rohner, and Alex Hearn. The publication of this article was funded by the Academic Articles Publication Fund of Universidad San Francisco de Quito USFQ.

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Authors and Affiliations

  1. Colegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito USFQ, Quito, 170157, Ecuador

    Alvaro Peña, Noel Pérez & Diego S. Benítez

  2. Colegio de Ciencias Biológicas y Ambientales “COCIBA”, Universidad San Francisco de Quito USFQ, Quito, 170157, Ecuador

    Alex Hearn

Authors
  1. Alvaro Peña
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  2. Noel Pérez
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  3. Diego S. Benítez
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  4. Alex Hearn
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Correspondence to Diego S. Benítez .

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Editors and Affiliations

  1. Universidad del Rosario, Bogotá, Colombia

    Alvaro David Orjuela-Cañón

  2. Universidad Autónoma de Occidente, Cali, Colombia

    Jesus Lopez

  3. Universidad de Antioquia, Medellín, Colombia

    Julián David Arias-Londoño

  4. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

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Peña, A., Pérez, N., Benítez, D.S., Hearn, A. (2021). Hammerhead Shark Species Monitoring with Deep Learning. In: Orjuela-Cañón, A.D., Lopez, J., Arias-Londoño, J.D., Figueroa-García, J.C. (eds) Applications of Computational Intelligence. ColCACI 2020. Communications in Computer and Information Science, vol 1346. Springer, Cham. https://doi.org/10.1007/978-3-030-69774-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-69774-7_4

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