MobileNets: Efficient Convolutional Neural Network for Identification of Protected Birds

Yulius Harjoseputro, Ign. Pramana Yuda, Kefin Pudi Danukusumo

Abstract


Wildlife trade is one of the main factors causing endangered bird species. In Indonesia, trade has caused 28 bird species to be classified in the endangered bird category. Protection efforts have been made with the establishment of 564 species of Indonesian birds as protected birds. For law enforcement, certainty is needed in the identification of these bird species. This study begins with a Forum of Discussion Groups from relevant institutions in Java and Bali to determine the types of protected birds that are prioritized to developed in this application. Based on the results of the Forum of Discussion Group, a bird photo dataset compiled using 17 categories or types of bird photos as prioritized in this study. The method used in this study is the Convolutional Neural Network (CNN) method, which combined the structure of MobileNet and the weight of the network that has previously trained using ImageNet. The results of this study are the differences of results between CNN standards and those combined with the structure of MobileNet. For better accuracy, using the CNN standard, which is around 98.38% for the accuracy of the training, while in terms of size, combined with MobileNet has a relatively smaller model size, which is 68 megabytes.

Keywords


wildlife; endangered; forum of discussion group; dataset; convolutional neural network; MobileNet; ImageNet.

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References


K. Lawson and A. Vines, Global Impacts of the Illegal Wildlife Trade, no. February. 2014.

A. Haryanta, D. N. Adhiasto, and N. Hardianto, Pendataan & Pengenalan Jenis Satwa Liar di Pasar Burung Yang Sering diperdagangkan. 2013.

P. P. Gavali, M. Prachi, A. Mhetre, M. Neha, and C. Patil, “Bird Species Identification using Deep Learning,†Int. J. Eng. Res. Technol., vol. 8, no. 04, pp. 68–72, 2019.

E. R. Bush, S. E. Baker, and D. W. Macdonald, “Global trade in exotic pets 2006-2012,†Conserv. Biol., vol. 28, no. 3, pp. 663–676, 2014.

P. Jepson and R. J. Ladle, “Bird-keeping in Indonesia: Conservation impacts and the potential for substitution-based conservation responses,†Oryx, vol. 39, no. 4, pp. 442–448, 2005.

J. Eaton et al., “Trade-driven extinctions and near-extinctions of avian taxa in Sundaic Indonesia,†Forktail, vol. 31, no. January 2015, pp. 0–12, 2016.

S. D. Das and A. Kumar, “Bird Species Classification using Transfer Learning with Multistage Training,†pp. 1–9, 2018.

L. Wilson-Wilde, “Wildlife crime: A global problem,†Forensic Sci. Med. Pathol., vol. 6, no. 3, pp. 221–222, 2010.

V. Nijman, “An overview of international wildlife trade from Southeast Asia,†Biodivers. Conserv., vol. 19, no. 4, pp. 1101–1114, 2010.

R. Dirzo, H. Young, M. Galetti, G. Ceballos, J. Nick, and B. Collen, “Defaunation in the antropocene_dirzo2014.pdf,†Science (80-. )., vol. 345, no. 6195, p. 401, 2014.

I. A. Ciampitti and J. Albers, “Agricultural Mobile Apps: A review and update of ID apps,†2015.

O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,†Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015.

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,†Int. J. Comput. Vis., vol. 88, no. 2, pp. 303–338, 2010.

T. Y. Lin et al., “Microsoft COCO: Common objects in context,†Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8693 LNCS, no. PART V, pp. 740–755, 2014.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,†Adv. neural Inf. Process. Syst., pp. 1097–1105, 2012.

Y. Lecun, L. Bottou, Y. Bengio, and P. Ha, “GradientBased Learning Applied to DocumentRecognition,†in Proceedings of the IEEE, 1998, no. November, pp. 1–46.

S. G. Lee, Y. Sung, Y. G. Kim, and E. Y. Cha, “Variations of AlexNet and GoogLeNet to improve Korean character recognition performance,†J. Inf. Process. Syst., vol. 14, no. 1, pp. 205–217, 2018.

B. P. Tóth and B. Czeba, “Convolutional neural networks for large-scale bird song classification in noisy environment,†CEUR Workshop Proc., vol. 1609, pp. 560–568, 2016.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,†J. Mach. Learn. Res., vol. 15, pp. 1929–1958, 2014.

A. Jain and B. K. Sharma, “Analysis of Activation Functions for Convolutional Neural Network based MNIST Handwritten Character Recognition,†Int. J. Adv. Stud. Sci. Res., vol. 3, no. 9, pp. 68–74, 2018.

W. S. Eka Putra, A. Y. Wijaya, and R. Soelaiman, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,†J. Tek. ITS, vol. 5, no. 1, pp. A65–A69, 2016.

A. Santoso and G. Ariyanto, “Implementasi Deep Learning Berbasis Keras Untuk Pengenalan Wajah,†Emit. J. Tek. Elektro, vol. 18, no. 01, pp. 15–21, 2018.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,†2014, pp. 1–14.

C. K. Dewa, A. L. Fadhilah, and A. Afiahayati, “Convolutional Neural Networks for Handwritten Javanese Character Recognition,†IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 12, no. 1, p. 83, 2018

A. G. Howard et al., “MobileNet s: Efficient Convolutional Neural Networks for Mobile Vision Applications,†2017.

B. Zhao, X. Wu, J. Feng, Q. Peng, and S. Yan, “Diversified Visual Attention Networks for Fine-Grained Object Classification,†IEEE Trans. Multimed., vol. 19, no. 6, pp. 1245–1256, 2017.




DOI: http://dx.doi.org/10.18517/ijaseit.10.6.10948

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