Abstract
Image classification is a major machine learning problem that has a wide range of applications in the real world. The Wellington Wildlife Camera Trap dataset contains images taken from vibration triggered cameras in sequences of three. State-of-the-art deep convolutional neural network (CNN) models, such as DenseNet-121 and ResNet-50, are unable to achieve the required accuracy of classification on this dataset. This research aims to improve the performance in multi-class classification tasks on the Wellington Dataset through a newly developed dual-input channel neural network. Our experiment results provide clear evidence that the new CNN model can achieve high accuracy and confidence on this challenging and scientifically important dataset. It is able to significantly reduce the amount of time required to manually classify wildlife images for conservation research in New Zealand.
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- 1.
Wellington Camera Trap dataset - http://lila.science/datasets/wellingtoncameratraps.
References
Anton, V., Hartley, S., Geldenhuis, A., Wittmer, H.U.: Monitoring the mammalian fauna of urban areas using remote cameras and citizen science. J. Urban Ecol. 4(1), juy002 (2018)
Anton, V., Hartley, S., Wittmer, H.U.: Evaluation of remote cameras for monitoring multiple invasive mammals in New Zealand. N. Z. J. Ecol. 42(1), 74–79 (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), pp. 858–862. IEEE (2014)
Chen, R., Little, R., Mihaylova, L., Delahay, R., Cox, R.: Wildlife surveillance using deep learning methods. Ecol. Evol. 9(17), 9453–9466 (2019)
Favorskaya, M., Pakhirka, A.: Animal species recognition in the wildlife based on muzzle and shape features using joint CNN. Procedia Comput. Sci. 159, 933–942 (2019)
Gomes, H.M., Barddal, J.P., Enembreck, F., Bifet, A.: A survey on ensemble learning for data stream classification. ACM Comput. Surv. (CSUR) 50(2), 1–36 (2017)
Hall, E.L., Kruger, R.P., Dwyer, S.J., Hall, D.L., Mclaren, R.W., Lodwick, G.S.: A survey of preprocessing and feature extraction techniques for radiographic images. IEEE Trans. Comput. 100(9), 1032–1044 (1971)
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, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Keskar, N.S., Mudigere, D., Nocedal, J., Smelyanskiy, M., Tang, P.T.P.: On large-batch training for deep learning: generalization gap and sharp minima. arXiv preprint arXiv:1609.04836 (2016)
Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. arXiv preprint arXiv:1901.06032 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings ICML, vol. 30, p. 3 (2013)
Mou, L., Ghamisi, P., Zhu, X.X.: Deep recurrent neural networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 55(7), 3639–3655 (2017)
Norouzzadeh, M.S., et al.: Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115(25), E5716–E5725 (2018)
Selfridge, O.G.: Pattern recognition and modern computers. In: Proceedings of the Western Joint Computer Conference, 1–3 March 1955, pp. 91–93 (1955)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., Packer, C.: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Sci. data 2, 150026 (2015)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Verma, G.K., Gupta, P.: Wild animal detection using deep convolutional neural network. In: Chaudhuri, B.B., Kankanhalli, M.S., Raman, B. (eds.) Proceedings of 2nd International Conference on Computer Vision & Image Processing. AISC, vol. 704, pp. 327–338. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7898-9_27
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
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Curran, B., Nekooei, S.M., Chen, G. (2022). Accurate New Zealand Wildlife Image Classification-Deep Learning Approach. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_51
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