Bird Detection using Siamese Neural Network
Rushil Gupta1, Shashank Pandey2, D Vanusha3
1Rushil Gupta, SRM Institute of Science and Technology, Tami Nadu.
2Shashank Pandey, SRM Institute of Science and Technology, Tami Nadu.
3D Vanusha, SRM Institute of Science and Technology, Tami Nadu.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 29, 2020. | Manuscript published on May 10, 2020. | PP: 1168-1171 | Volume-9 Issue-7, May 2020. | Retrieval Number: E2468039520/2020©BEIESP | DOI: 10.35940/ijitee.E2468.059720
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The proposed system uses deep neural networks for identifying bird species. The model will be trained on bird images that are coming in the endangered species category. The application can also handle new data points, unlike existing systems that require model re-training for accommodating new data. The system can identify bird species in a large view of the image. The model will be trained using a convolutional neural network-based architecture called Siamese Network. This network is also called one-shot learning which means that it requires only few training example for each class. Existing models use image processing techniques or vanilla convolutional neural networks for classifying bird images. These models cannot accommodate new images and have to be retrained to do so. There is no commercially available system that can detect a species of bird in high resolution / large image. While in the Siamese network we only have to add new data, there is no need to retraining the neural network.
Keywords: Neural networks, Model deployment, Edge AI, Deep learning.
Scope of the Article: Deep learning