Abstract
Reliable identification of bird species is a critical task for many applications, such as conservation biology, biodiversity assessments, and monitoring bird populations. However, identifying birds in the wild by visual observation can be time-consuming and prone to errors. There is a growing need for efficient and accurate bird recognition methods that can help researchers and conservationists identify bird species quickly and reliably. In this paper, we present a comparative analysis of the performance of state-of-the-art deep convolutional neural networks on a significantly sized bird dataset. Our goal is to develop a more accurate and efficient bird recognition method that can be deployed on edge computing devices. The results show that lightweight networks as EfficientNetB0 provide a great accuracy (more than 97%) and low time of response with a small demand for technological resources. Our findings could provide a reliable means of identifying bird species in the wild, which is essential for many conservation and management efforts.
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References
Upton, S.: Biodiversity and ecosystems (2014). Organisation for Economic Co-operation and Development. https://www.oecd.org/env/resources/OECD-work-on-biodiversity-and-ecosystems.pdf
Organisation for economic co-operation and development, results of the survey on the coefficients applied to RIO marker data when reporting to the un conventions on climate change and biodiversity, DCD/DAC/STAT(2020) 41/REV2
Ohsawa, T.: Idea paper: how are ecosystem services related to biodiversity and ecological integrity in each site under climate change? First published: 10 March 2022, Volume 37, Issue 4. https://doi.org/10.1111/1440-1703.12302
Weiskopf, S.R., et al.: Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States, science of the total environment, vol. 733, 137782 (2020). ISSN 0048–9697. https://doi.org/10.1016/j.scitotenv.2020.137782
Jakariaa, A., Ferdinandus Pardedea, H.: Comparison of classification of birds using lightweight deep convolutional neural networks. Jurnal Elektronika dan Telekomunikasi (JET), Vol. 22, No. 2, December 2022, pp. 87–94. Accredited by KEMDIKBUDRISTEK, Decree No: 158/E/KPT/2021. https://doi.org/10.55981/jet.503
Tabur, Ali, M., Ayvaz, Y.: Ecological Importance of Birds (2010)
Belaire, J.A., et al.: Urban residents’ perceptions of birds in the neighborhood: Biodiversity, cultural ecosystem services, and disservices. The Condor. 117, 192–202. (2015). https://doi.org/10.1650/CONDOR-14-128.1
Narasimhan, R., et al.: Simultaneous segmentation and classification of bird song using CNN. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, pp. 146–150 (2017). https://doi.org/10.1109/ICASSP.2017.7952135
Jancovic, P., Köküer, M.: Bird species recognition using unsupervised modeling of individual vocalization elements. IEEE/ACM Trans. Audio, Speech, Lang. Process. 27(5), 932–947 (2019). https://doi.org/10.1109/TASLP.2019.2904790
Gerry, "BIRDS 510 SPECIES- IMAGE CLASSIFICATION", views - 280689, downloads - 40432. https://www.kaggle.com/datasets/gpiosenka/100-bird-species
Sungsiri, A., et al.: The classification of edible-nest swiftlets using deep learning. In: 2022 6th International Conference on Information Technology (InCIT), Nonthaburi, Thailand, pp. 404–409 (2022). https://doi.org/10.1109/InCIT56086.2022.10067665
Al-Showarah, S., Qbailat, S.: Birds identification system using deep learning. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 12(4) (2021)
Huang, Y.P., Basanta, H.: Bird image retrieval and recognition using a deep learning platform. IEEE Access 7, 66980–66989 (2019). https://doi.org/10.1109/ACCESS.2019.2918274
Chin, T.-W., Ding, R., Zhang, C., Marculescu, D.: Towards efficient model compression via learned global ranking (2019). https://doi.org/10.48550/arXiv.1904.12368
Zhu, W., et al.: Evolutionary convolutional neural networks using ABC. In: ICMLC ’19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing, 156–162. https://doi.org/10.1145/3318299.3318301
Yang, L., et al.: On hyperparameter optimization of machine learning algorithms: theory and practice (2020). Department of Electrical and Computer Engineering, The University of Western Ontario, arXiv:2007.15745v3 [cs.LG] 5 Oct 2022
Van Horn, G., et al.: Exploring fine-grained audiovisual categorization with the SSW60 dataset (2022). https://doi.org/10.48550/arXiv.2207.10664
Huang, G., et al.: Densely connected convolutional networks (2017). https://doi.org/10.48550/arXiv.1608.06993
Xiang, W., et al.: Birds detection in natural scenes based on improved faster RCNN. Appl. Sci. 12, 6094 2022. https://doi.org/10.3390/app 12126094
Rahman, Md. M., et al.: Recognition of local birds of bangladesh using MobileNet and Inception-v3. Int. J. Adv. Comput. Sci. Appl. 11 (2020). https://doi.org/10.14569/ijacsa.2020.0110840
Bold, N., et al.: Bird species classification with audio-visual data using CNN and multiple kernel learning. In: 2019 International Conference on Cyberworlds (CW), Kyoto, Japan, pp. 85–88 (2019). https://doi.org/10.1109/CW.2019.00022
Liu, H., et al.: TransIFC: invariant cues-aware feature concentration learning for efficient fine-grained bird image classification. IEEE Trans. Multimedia (2023). https://doi.org/10.1109/TMM.2023.3238548
Nawaz, S., et al.: Are these birds similar: learning branched networks for fine-grained representations. In: 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ), Dunedin, New Zealand, pp. 1–5 (2019). https://doi.org/10.1109/IVCNZ48456.2019.8960960
Wu, P., et al.: Classification of birds based on weighted fusion model. In: 2021 7th International Conference on Big Data Computing and Communications (BigCom) 90–97 (2021)
Alswaitti, M., et al.: Effective classification of birds’ species based on transfer learning. Int. J. Electrical Comput. Eng. (IJECE), Vol. 12, No. 4, pp. 4172 4184 (2022). ISSN: 2088–8708, https://doi.org/10.11591/ijece.v12i4.pp4172-4184
Bold, N., et al.: Cross-domain deep feature combination for bird species classification with audio-visual data. In: 2019 Volume E102.D(10), pp. 2033–2042 (2019). https://doi.org/10.1587/transinf.2018EDP7383
Huang, Y.P., Basanta, H.: Recognition of endemic bird species using deep learning models. IEEE Access 9, 102975–102984 (2021). https://doi.org/10.1109/ACCESS.2021.3098532
Azorín-López, J., Saval-Calvo, M., Fuster-Guilló, A., García-Rodríguez, J.: Human behaviour recognition based on trajectory analysis using neural networks. In: The 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA, pp. 1–7 (2013). https://doi.org/10.1109/IJCNN.2013.6706724
Azorin-Lopez, J., Saval-Calvo, M., Fuster-Guillo, A., Garcia-Rodriguez, J., Cazorla, M., Signes-Pont, M.T.: Group activity description and recognition based on trajectory analysis and neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 2016, pp. 1585–1592 (2016). https://doi.org/10.1109/IJCNN.2016.7727387
Azorin-Lopez, J., Saval-Calvo, M., Fuster-Guillo, A., Garcia-Rodriguez, J.: A novel prediction method for early recognition of global human behaviour in image sequences. Neural Process. Lett. 43(2), 363–387 (2015). https://doi.org/10.1007/s11063-015-9412-y
Acknowledgment
We would like to express our gratitude to the “A way of making Europe" European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for their support of this work under the “CHAN-TWIN" project (grant TED2021-130890B-C21) and AICARE project (grant SPID202200X139779IV0). We are also thankful for the funding received from the HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE (Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning). Additionally, we extend our appreciation to Nvidia for their generous hardware donations, which made these experiments possible.
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Teterja, D. et al. (2023). A Performance Evaluation of Lightweight Deep Learning Approaches for Bird Recognition. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14134. Springer, Cham. https://doi.org/10.1007/978-3-031-43085-5_26
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