| Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01057 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/e3sconf/202343001057 | |
| Published online | 06 October 2023 | |
Automated System for Bird Species Identification Using CNN
1 Department of Computer Science, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India
2 Uttaranchal School of Computing Sciences, Uttaranchal University, Dehradun, 248007, India
* Corresponding author: siri1686@grietcollege.com
There are around 11000 different bird species in the globe. Rarely are certain bird species encountered. Bird identification is a challenging task that usually leads to unclear labelling. When presented a picture of a bird, even professional bird watchers differ on the species. Despite having the same basic components across all bird species, form and appearance can vary greatly. Intraclass variance is substantial due to variations in lighting and backdrop, as well as a wide range of instances. Additionally, visual recognition of birds by humans is more comprehensible than audible recognition of birds. Consequently, the convolutional neural networks(CNN) is utilized for an automated bird species identification system. CNNs are a powerful Deep Learning ensemble that have shown to be effective in image processing. The dataset is used for both training and testing of a CNN system that classifies bird species. This will lead to quick identification of the bird species using an automated process.
© The Authors, published by EDP Sciences, 2023
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