DETECTION OF BEHAVIOUR AND POSTURE OF SHEEP BASED ON YOLOv3

Abstract

The behaviour and posture of animals are closely related to their physiological conditions. To some extent, we can judge their physiological activity by their behaviour and posture. Sheep’s behaviour change significantly during illness or parturition, these behaviours are composed of simple postures, such as standing, eating, lying down. This paper takes the YOLOv3 algorithm as the core technology. It extracts features of sheep’s behaviour and posture through constructing the deep network structure, and uses the pyramid feature fusion and multi-scale prediction to detect the behaviour and posture of sheep. The experimental results show that the training model can effectively detect the three behaviours and postures of sheep: standing, eating, and lying down. The mean average precision is 92.47%. This experiment can be used as a basic technology to judge the physiological activities of sheep. It can be applied to the intelligence of animal husbandry, and has a broad application prospect.