An Integrated Framework for Bird Recognition Using Dynamic Machine Learning-Based Classification

Bird recognition in computer vision poses two main challenges: high intra-class variance and low inter-class variance. High intra-class variance refers to the significant variation in the appearance of individual birds within the same species. Low inter-class variance refers to the limited visual differences between distinct bird species. In this paper, we propose a robust integrated framework for bird recognition using a dynamic machine learning-based technique. Our system is designed to identify over 11,000 species of birds based on multiple components. As part of this work, we propose two public datasets. The first one (E-Moulouya BDD) contains over 13k images of birds for detection tasks. While the second one (LaSBiRD) contains about 5M labelled images of 11k species. Our experiments yielded promising results, indicating the impressive performance of our system in detecting and classifying birds. With a mAP of 0.715 for detection and an accuracy rate of 96% for classification.