Bird Family Recognition

Authors

Presenter(s)

Soham Chousalkar, Kasturi Avinash Jamale, Jayanth Merakanapalli

Comments

9:40-10:00, LTC Studio

Files

Description

In this research, we present a novel deep learning-based approach for bird detection and classification. Using YouTube videos as a data source, we train a model capable of accurately identifying bird species in diverse environments. Our dataset consists of 20 bird species, each categorized into two subclasses: parent and chick. Leveraging YOLO models, our system effectively detects and classifies birds under varying environmental conditions. The proposed method demonstrates high classification accuracy, contributing to advancements in automated bird identification. This work has significant applications in ecological monitoring and conservation efforts, aiding researchers in tracking and studying avian populations.

Publication Date

4-23-2025

Project Designation

Graduate Research

Primary Advisor

Tam Nguyen

Primary Advisor's Department

Computer Science

Keywords

Stander Symposium, College of Arts and Sciences

Institutional Learning Goals

Diversity; Vocation; Practical Wisdom

Recommended Citation

"Bird Family Recognition" (2025). Stander Symposium Projects. 4034.
https://ecommons.udayton.edu/stander_posters/4034