DETECTION OF BEHAVIOUR AND POSTURE OF SHEEP BASED ON YOLOv3
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Published:2021-08-31
Issue:
Volume:
Page:457-466
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ISSN:2068-2239
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Container-title:INMATEH Agricultural Engineering
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language:en
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Short-container-title:INMATEH
Author:
Deng Xuefeng1, Yan Xiaoli2, Hou Yiming1, Wu Hui1, Feng Chenru1, Chen Lingyu1, Bi Maoxing1, Shao Yi1
Affiliation:
1. College of Information Science and Engineering, Shanxi Agricultural University, Taigu / China 2. Taiyuan Jinshan Middle School, Taiyuan / China
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.
Publisher
INMA Bucharest-Romania
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering,Food Science
Reference17 articles.
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Cited by
2 articles.
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