Unmanned Aerial Vehicle-Based Animal Detection via Hybrid CNN and LSTM Model

In this paper, we present a novel approach for wildlife animal detection in natural habitats using a deep learning model. Our model leverages a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, trained on aerial imagery captured by unmanned aircraft. We conducted a comprehensive 50-epoch training and testing regimen, and the results reveal the model's learning trajectory and performance. The findings demonstrate a progressive increase in testing accuracy, indicative of the model's improved ability to recognize and classify a diverse range of animal species. This research contributes to wildlife conservation and monitoring efforts, particularly in remote or inaccessible areas, by providing an automated and accurate means of animal detection. The model's proficiency in real-world scenarios is highlighted, making it a valuable tool for ecological research and conservation initiatives.