Abstract
Recently, not only studies on inanimate objects, but also deep learning-based image recognition studies on animals and plants are being actively conducted. Animals and plants designated by CITES are protected internationally. At airports and ports, it takes a lot of time and money due to manual inspection at customs clearance. Using this vulnerability, smugglers illegally import animals and plants belonging to CITES. To solve this problem, in this paper, we propose a method for classifying parrot species belonging to CITES by detecting objects based on deep neural networks. The SSD model was used for object detection, and the data augmentation technique was also applied to prevent overfitting. Using the trained model, parrot classification performance was measured as the mAP of about 95.7%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bush, E.R., Baker, S.E., Macdonald, D.W.: Global trade in exotic pets 2006–2012. Conserv. Biol.Conserv. Biol. 28(3), 663–676 (2014)
Kang, E., Han, Y., Oh, I.S.: Mushroom image recognition using convolutional neural network and transfer learning. KIISE Trans. Comput. Pract. 24(1), 53–57 (2018)
Labao, A.B., Naval Jr., P.C.: Cascaded deep network systems with linked ensemble components for underwater fish detection in the wild. Ecol. Inf. 52, 103–121 (2019)
Jang, W., kim, T., Nam, U., Lee, E.C.: Image segmentation and identification of parrot by using Faster R-CNN. In: Proceeding of ICNCT 2019, 12–14, pp. 91–92 (2019)
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. pp. 580–587 (2014)
Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1440–1448 (2015)
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)
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)
Liu, W., et al.: Ssd: Single shot multibox detector.In: European Conference on Computer Vision, pp. 21–37. Springer, Cham (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ICLR, CoRR arXiv:1409.1556 (2015)
Acknowledgement
This work was financially supported by a Grant (2018000210004) from the Ministry of Environment, Republic of Korea.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Jang, W., Seong, S.W., Kim, C.B., Lee, E.C. (2021). Image Identification of Multiple Parrot Species Belonging to CITES Using Deep Neural Networks. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12616. Springer, Cham. https://doi.org/10.1007/978-3-030-68452-5_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-68452-5_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68451-8
Online ISBN: 978-3-030-68452-5
eBook Packages: Computer ScienceComputer Science (R0)