摘要
针对目前猪只爬跨行为自动化检测程度较低的问题,提出了一种基于MaskR-CNN的猪只爬跨行为识别算法。首先获取猪只俯视图像,利用Labelme制作数据集标签,引入迁移学习方法训练ResNet-FPN网络,获取猪只分割结果,并提取每个样本中的mask像素面积。提取每个样本中的最小mask像素面积作为爬跨行为识别的经验样本集,确定爬跨行为界定阈值。利用测试集分别测试猪只分割网络模型及爬跨行为识别算法,结果表明,猪只分割网络模型的分割准确率为94%,爬跨行为识别算法准确率为94.5%。本算法能够自动有效地检测猪只爬跨行为,可为牲畜养殖自动化提供支持。
The mounting behavior of pigs is generally manifested as a pig puts two front legs on the body or head of another pig which stays lying or dodged quickly.Mounting between pigs often causes epidermal wounds and even fractures,which reduces animal welfare and affects the economic benefits.Therefore,it is necessary to isolate the mounting pigs in time.In view of the low degree of automation of current mounting behavior detection of pigs,an algorithm based on Mask R-CNN was proposed to recognize the mounting behavior of pigs.Firstly,the top view videos of pigs were shot,and the dataset labels were made by Labelme.The transfer learning was applied to train the ResNet-FPN network to obtain the pig segmentation result and extract the mask pixel area in each sample.The value of the minimum mask pixel area in each sample was extracted in order to build an empirical sample set for mounting behavior recognition,and the discriminant threshold of the mounting behavior of pigs was determined.In the experiment,the test dataset was used to evaluate the pig segmentation network model and the mounting behavior recognition algorithm.The segmentation accuracy of the network was 94%,and the accuracy of the mounting behavior recognition algorithm was 94.5%.The experimental results showed that the algorithm can effectively detect the mounting behavior of pigs and provide support for livestock breeding automation.
作者
李丹
张凯锋
李行健
陈一飞
李振波
蒲东
LI Dan;ZHANG Kaifeng;LI Xingjian;CHEN Yifei;LI Zhenbo;PU Dong(College of Information and Electronics Engineering,China Agricultural University,Beijing 100083,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2019年第B07期261-266,275,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重大科技基础设施项目(4444-10099609)
