Pose estimation of sow and piglets during free farrowing using deep learning

Automatic and real-time pose estimation is important in monitoring animal behavior, health, and welfare. In this paper, we utilized pose estimation for monitoring the farrowing process to prevent piglet mortality and preserve the health and welfare of the sow. State-of-the-art Deep Learning (DL) methods have lately been used for animal pose estimation. This paper aims to probe the generalization ability of five common DL networks (ResNet50, ResNet101, MobileNet, EfficientNet, and DLCRNet) for sow and piglet pose estimation. These architectures predict the body parts of several piglets and the sow directly from input video sequences. Real farrowing data from a commercial farm was used for training and validation of the proposed networks. The experimental results demonstrated that MobileNet was able to detect seven body parts of the sow with a median test error of 0.61 pixels.