Abstract:
Birds, as a diverse and integral component of our natural world, play a pivotal role in maintaining
the balance and health of ecosystems. Their presence influences not only the environment but also
various aspects of human life, making the accurate identification and understanding of birds
crucial. Bird detection and classification from images is a challenging task with diverse
applications, ranging from wildlife conservation and ecological studies to urban planning and
agriculture. This research paper aims to explore the use of deep learning techniques for accurately
detecting and classifying birds in images. Deep learning is used in many fields [1], like speech,
image recognition, drug discovery and toxicology, customer management, recommendation
systems, bioinformatics, NLP etc. In our paper delve into various state-of-the-art deep learning
architectures, including EfficientNetB7, MobileNetV3, ResNet101. The research involves the
preparation of a comprehensive dataset of bird images and evaluates the performance of different
models based on various metrics, such as precision, recall, and F1-score. By enhancing our
capacity to identify and comprehend birds, we strengthen our ability to protect and conserve the
intricate web of life on our planet. Furthermore, we discussed the challenges encountered during
the process and proposed potential avenues for future research to enhance bird detection
capabilities in real-world scenarios.
