Addressing significant challenges for animal detection in camera trap images: a novel deep learning-based approach


  • Rovero, F., Kays, R. Camera trapping for conservation. In Conservation Technology, chap. 10 (eds. Wich, S. & Piel, A. K.) 79–101 (Oxford University Press, 2021).

  • Boitani, L. Camera Trapping for Wildlife Research (Pelagic Publishing Ltd., 2016).

  • Rovero, F., Tobler, M. & Sanderson, J. Camera trapping for inventorying terrestrial vertebrates. Manual on field recording techniques and protocols for all taxa biodiversity inventories and monitoring. Belgian Natl. Focal Point Glob. Taxon. Initiat. 8, 100–128 (2010).


    Google Scholar
     

  • Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115, E5716–E5725. https://doi.org/10.1073/pnas.1719367115 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapr: an r package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462 (2016).

    Article 

    Google Scholar
     

  • Young, S., Rode-Margono, J. & Amin, R. Software to facilitate and streamline camera trap data management: A review. Ecol. Evol. 8, 9947–9957 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vélez, J. et al. An evaluation of platforms for processing camera-trap data using artificial intelligence. Methods Ecol. Evol. 14, 459–477 (2023).

    Article 

    Google Scholar
     

  • Hendry, H., Mann, C. Camelot–intuitive software for camera trap data management. BioRxiv 203216 (2017).

  • Chalmers, C., Fergus, P., Wich, S., Montanez, A.C. Conservation ai: Live stream analysis for the detection of endangered species using convolutional neural networks and drone technology. arXiv preprint arXiv:1910.07360 (2019).

  • Tabak, M. A. et al. Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: Mlwic2. Ecol. Evol. 10, 10374–10383 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fennell, M., Beirne, C. & Burton, A. C. Use of object detection in camera trap image identification: Assessing a method to rapidly and accurately classify human and animal detections for research and application in recreation ecology. Glob. Ecol. Conserv. 35, e02104 (2022).


    Google Scholar
     

  • Peng, J. et al. Wild animal survey using uas imagery and deep learning: modified faster r-cnn for kiang detection in tibetan plateau. ISPRS J. Photogramm. Remote. Sens. 169, 364–376 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Zhu, H., Tian, Y. & Zhang, J. Class incremental learning for wildlife biodiversity monitoring in camera trap images. Eco. Inform. 71, 101760 (2022).

    Article 

    Google Scholar
     

  • Villa, A. G., Salazar, A. & Vargas, F. Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks. Eco. Inform. 41, 24–32 (2017).

    Article 

    Google Scholar
     

  • Beery, S., Morris, D. & Yang, S. Efficient pipeline for camera trap image review. 1907, 06772 (2019).

  • Jocher, G. et al. ultralytics/yolov5: v3.0. Zenodo (2020).

  • Hughey, L. F., Hein, A. M., Strandburg-Peshkin, A. & Jensen, F. H. Challenges and solutions for studying collective animal behaviour in the wild. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170005 (2018).

    Article 

    Google Scholar
     

  • Wu, X., Sahoo, D. & Hoi, S. C. Recent advances in deep learning for object detection. Neurocomputing 396, 39–64 (2020).

    Article 

    Google Scholar
     

  • Tong, K., Wu, Y. & Zhou, F. Recent advances in small object detection based on deep learning: A review. Image Vis. Comput. 97, 103910 (2020).

    Article 

    Google Scholar
     

  • Guo, Y. et al. Varied channels region proposal and classification network for wildlife image classification under complex environment. IET Image Proc. 14, 585–591 (2020).

    Article 

    Google Scholar
     

  • Gao, M., Du, Y., Yang, Y. & Zhang, J. Adaptive anchor box mechanism to improve the accuracy in the object detection system. Multimed. Tools Appl. 78, 27383–27402 (2019).

    Article 

    Google Scholar
     

  • Miao, Z. et al. Insights and approaches using deep learning to classify wildlife. Sci. Rep. 9(1), 1–9 (2019).

  • Zhao, Z.-Q., Zheng, P., Xu, S.-T. & Wu, X. Object detection with deep learning: A review. IEEE Trans. Neural Netw. Learn. Syst. 30, 3212–3232 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, 2980–2988 (2017).

  • Liu, W. et al. Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, 21–37 (Springer, 2016).

  • Yoo, D., Park, S., Lee, J.-Y., Paek, A.S., So Kweon, I. Attentionnet: Aggregating weak directions for accurate object detection. In Proceedings of the IEEE International Conference on Computer Vision, 2659–2667 (2015).

  • Girshick, R., Donahue, J., Darrell, T., Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580–587 (2014).

  • Ren, S., He, K., Girshick, R., Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 28 (2015).

  • Dai, J., Li, Y., He, K., Sun, J. R-fcn: Object detection via region-based fully convolutional networks. Adv. Neural Inf. Process. Syst. 29 (2016).

  • Janzen, M., Ritter, A., Walker, P. D. & Visscher, D. R. Eventfinder: a program for screening remotely captured images. Environ. Monit. Assess. 191, 1–10 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Falzon, G. et al. Classifyme: a field-scouting software for the identification of wildlife in camera trap images. Animals 10, 58 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Parikh, M., Patel, M. & Bhatt, D. Animal detection using template matching algorithm. Int. J. Res. Mod. Eng. Emerg. Technol 1, 26–32 (2013).


    Google Scholar
     

  • Swinnen, K. R., Reijniers, J., Breno, M. & Leirs, H. A novel method to reduce time investment when processing videos from camera trap studies. PLoS One 9, e98881 (2014).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Antônio, W. H., Da Silva, M., Miani, R. S. & Souza, J. R. A proposal of an animal detection system using machine learning. Appl. Artif. Intell. 33, 1093–1106 (2019).

    Article 

    Google Scholar
     

  • Yu, X. et al. Automated identification of animal species in camera trap images. EURASIP J. Image Video Process. 2013, 1–10 (2013).

    Article 
    ADS 

    Google Scholar
     

  • Yousif, H., Yuan, J., Kays, R., He, Z. Fast human-animal detection from highly cluttered camera-trap images using joint background modeling and deep learning classification. In 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 1–4 (IEEE, 2017).

  • Verma, G.K., Gupta, P. Wild animal detection using deep convolutional neural network. In Proceedings of 2nd International Conference on Computer Vision & Image Processing: CVIP 2017, vol. 2, 327–338 (Springer, 2018).

  • Chen, G., Han, T.X., He, Z., Kays, R., Forrester, T. Deep convolutional neural network based species recognition for wild animal monitoring. In 2014 IEEE International Conference on Image Processing (ICIP), 858–862 (IEEE, 2014).

  • Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115, E5716–E5725 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zualkernan, I. et al. An iot system using deep learning to classify camera trap images on the edge. Computers 11, 13 (2022).

    Article 

    Google Scholar
     

  • Zhao, B., Feng, J., Wu, X. & Yan, S. A survey on deep learning-based fine-grained object classification and semantic segmentation. Int. J. Autom. Comput. 14, 119–135 (2017).

    Article 

    Google Scholar
     

  • Zett, T., Stratford, K. J. & Weise, F. J. Inter-observer variance and agreement of wildlife information extracted from camera trap images. Biodivers. Conserv. 31, 3019–3037 (2022).

    Article 

    Google Scholar
     

  • Korsch, D., Bodesheim, P., Denzler, J. Classification-specific parts for improving fine-grained visual categorization. In Pattern Recognition: 41st DAGM German Conference, DAGM GCPR 2019, Dortmund, Germany, September 10–13, 2019, Proceedings 41, 62–75 (Springer, 2019).

  • Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S. Large scale fine-grained categorization and domain-specific transfer learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4109–4118 (2018).

  • Rodner, E. et al. Fine-grained recognition datasets for biodiversity analysis. 1507, 00913 (2015).

  • Gebru, T., Hoffman, J., Fei-Fei, L. Fine-grained recognition in the wild: A multi-task domain adaptation approach. In Proceedings of the IEEE International Conference on Computer Vision, 1349–1358 (2017).

  • Rigoudy, N. et al. The deepfaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images. Eur. J. Wildl. Res. 69, 113 (2023).

    Article 

    Google Scholar
     

  • Gooliaff, T. & Hodges, K. E. Measuring agreement among experts in classifying camera images of similar species. Ecol. Evol. 8, 11009–11021 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leorna, S., Brinkman, T. Human vs. machine: Detecting wildlife in camera trap images. Ecol. Inform. 72, 101876 (2022).

  • VÃlez, J. et al. Choosing an appropriate platform and workflow for processing camera trap data using artificial intelligence (2022). 2202.02283.

  • Szegedy, C. et al. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9 (2015).

  • Krizhevsky, A., Sutskever, I., Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012).

  • He, K., Zhang, X., Ren, S., Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778 (2016).

  • Simonyan, K., Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).

  • Chollet, F. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251–1258 (2017).

  • Lumini, A. & Nanni, L. Deep learning and transfer learning features for plankton classification. Eco. Inform. 51, 33–43 (2019).

    Article 

    Google Scholar
     

  • Deng, J. et al. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255 (IEEE, 2009).



  • Source link

    More From Forest Beat

    For many island species, the next tropical cyclone may be their...

    When a major cyclone tears through an island nation, all efforts rightly focus on saving human lives and restoring...
    Biodiversity
    3
    minutes

    Mapping benthic habitats in Bohai Bay, China

    Habitat classification schemeDeveloping a benthic habitat classification scheme is a fundamental step in benthic habitat mapping, providing a structured framework for organizing and...
    Biodiversity
    8
    minutes

    Effect of climate on traits of dominant and rare tree species...

    Institute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, SwitzerlandIris Hordijk, Chelsea Chisholm, Daniel S. Maynard & Thomas W. CrowtherWageningen University and Research, Wageningen,...
    Biodiversity
    15
    minutes

    CheloniansTraits: a comprehensive trait database of global turtles and tortoises

    Lyson, T. R. et al. Fossorial origin of the turtle shell. Current Biology 26, 1887–1894 (2016).CAS  PubMed  ...
    Biodiversity
    6
    minutes
    spot_imgspot_img