Classification of Animal Images with modified CNN model
Abstract
In the age of modern artificial intelligence, new methodologies are evolving day by day for species classification. There is some need for categorization of the animal for the preservation and restoration of species. Due to this, there are several techniques for the identification of animals out of the deep learning methods that are most useful for animal classification for their images. In this study, the modified and improved convolutional neural network (CNN) has been employed. This study is the identification methodologies of the ten animals which are cat, dog, tiger, lion, sheep, rhino, cheetah, elephant, squirrel, and panda. These animals need to be identified by the artificial intelligence-based system for the large-scale preservation system and the accuracy obtained by the modified CNN is this. In the future, this study is going to evolve deep learning for human-less classification systems and this study will maintain the balance between the machine and animals in restricted areas that humans can’t reach there. Animal classification is one of the core problems in Computer vision. A lot of attention has been associated with Deep Learning, specifically neural networks such as CNN. This animal classification model gives an accuracy of 95%.
Downloads
References
Mikołajczyk, A., & Grochowski, M. (2018, May). Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW) (pp. 117-122). IEEE.
Okafor, E., Smit, R., Schomaker, L., & Wiering, M. (2017, July). Operational data augmentation in classifying single aerial images of animals. In 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 354-360). IEEE.
Prudhivi, L., Narayana, M., Subrahmanyam, C., & Krishna, M. G. (2021). Animal species image classification. Materials Today: Proceedings.
Tiwari, V., Pandey, C., Dwivedi, A., & Yadav, V. (2020, December). Image classification using deep neural network. In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) (pp. 730-733). IEEE.
Taheri, S., & Toygar, Ö. (2018). Animal classification using facial images with score‐level fusion. IET computer vision, 12(5), 679-685.
Willi, M., Pitman, R. T., Cardoso, A. W., Locke, C., Swanson, A., Boyer, A., ... & Fortson, L. (2019). Identifying animal species in camera trap images using deep learning and citizen science. Methods in Ecology and Evolution, 10(1), 80-91.
Yousif, H., Yuan, J., Kays, R., & He, Z. (2017, May). 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) (pp. 1-4). IEEE.
