摘要
猪只饮水行为往往与猪舍环境的舒适度以及猪只的健康水平密切相关,实时地监控猪栏中猪只饮水状况对猪舍管理和提高养殖福利具有重要意义。目前,主要采用RFID和机器视觉两种技术自动识别饮水行为,RFID方式需要给猪只佩戴耳标,存在对猪身有侵入和不便操作的缺点,机器视觉方法能非接触式监测和提取猪只饮水行为,具有低成本易实施的优点。本文实现了基于机器视觉的猪只饮水行为自动识别,首先通过传统的阈值分割方法得到二值化图像来实现猪只从背景中的提取,接着引入图像占领指数对猪只饮水行为进行预判,最后利用深度学习方法构造猪只头部检测器,更精准地判定饮水行为的发生。试验表明,该方法在本文构建的饮水视频数据集中识别正确率为92.11%,且能识别饮水猪只的身份,可应用到实际的猪只生产过程中辅助管理决策。
Pig drinking behavior is closely related to pig＇ s physical condition and piggery environment. Recording such data continuously is beneficial to the pig industry. However, it is difficult to get the detailed drinking data of each pig manually. An automated method is expected. RFID is used to detect pig drinking behavior recently. But this approach invades pigs and the piggery needs to be equipped with auxiliary facilities. There is no such concern by using video monitoring. Therefore, using machine vision to recognize pig drinking behavior was proposed. Firstly, to distinguish pigs from the background, threshold segmentation was used to get a binary image, in which pixels belonged to pigs were assigned to 1 and others were assigned to 0. From the binary image, each pig＇ s centroid and angle were computed and used to decide whether a pig was static or not. Drinking behavior is likely to happen when a pig stays in the drinking zone. Secondly, occupation index was computed to determine if a static pig was closed to the drinking nipple. Drinking behavior could be preliminarily judged through this way. Thirdly, a pig head detector was implemented by using deep learning algorithm to accurately confirm the occurrence of pig drinking behavior. At last, to confirm which pig was performing the drinking behavior, a pig identification detector was implemented. Through the multi-step judgment, pig drinking behavior can be recognized precisely. Experiment showed that the precision rate of the proposed algorithm in the video data set was 92. 1 1% , which was suitable to aid managerial decision making in pig production.
作者
杨秋妹
肖德琴
张根兴
YANG Qiumei;XIAO Deqin;ZHANG Genxing(College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2018年第6期232-238,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2017YFD0701601)
广东省科技计划项目(2015A020209149)
关键词
猪只
饮水行为
图像分割
图像占领指数
深度学习
pig
drinking behavior
image segmentation
image occupation index
deep learning
