Animal Detection

Animal detection, primarily using computer vision, aims to automatically identify and locate animals in images and videos, supporting applications like precision livestock farming and wildlife monitoring. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), often incorporating techniques like YOLO for object detection and pose estimation. A significant challenge is the limited availability of high-quality labeled datasets, leading to increased use of synthetic data generation to augment training. This technology has broad implications for improving animal welfare, optimizing agricultural practices, and advancing ecological research.