Recognizing Wild Animals from Camera Trap Images Using Deep Learning

Animal detection using deep learning leverages advanced machine learning to identify and classify animals in images or videos. This technology has significant implications for wildlife conservation, animal population monitoring, and automated surveillance systems. Deep learning models, especially Convolutional Neural Networks (CNNs), have shown great success in image classification tasks, including animal detection. These models can learn complex patterns and features from large datasets, enabling them to distinguish between species with high accuracy. This project presents an approach to animal detection, specifically focusing on buffalos, using CNNs. The VGG16 model, pre-trained on the ImageNet dataset, is the main tool used, showcasing the strength of transfer learning. The process starts with loading images, which are then preprocessed to match the VGG16 input size. Once formatted, the images are fed into the model for prediction, and the results are decoded to provide clear classification outcomes. This paper highlights the effectiveness of CNNs, particularly the VGG16 model, in animal detection tasks and emphasizes the potential of transfer learning. By using pre- trained models trained on diverse datasets, high accuracy can be achieved in image classification tasks, even when target categories differ from the original dataset.