在利用视频监控技术对群养猪只进行自动行为监测时,对猪只准确定位并辨别其头尾位置对提高监测水平至关重要,基于此提出一种基于YOLO v3(You only look once v3)模型与图结构模型(Pictorial structure models)的猪只头尾辨别方法。首先...在利用视频监控技术对群养猪只进行自动行为监测时,对猪只准确定位并辨别其头尾位置对提高监测水平至关重要,基于此提出一种基于YOLO v3(You only look once v3)模型与图结构模型(Pictorial structure models)的猪只头尾辨别方法。首先,利用基于深度卷积神经网络的YOLO v3目标检测模型,训练猪只整体及其头部和尾部3类目标的检测器,从而在输入图像中获得猪只整体及头尾部所有的检测结果;然后,引入图结构模型,描述猪只的头尾结构特征,对每个猪只整体检测矩形框内的头尾部位组合计算匹配得分,选择最优的部位组合方式;对部分部位漏检的情况,采取阈值分割与前景椭圆拟合的方法,根据椭圆长轴推理出缺失部位。在实际猪场环境下,通过俯拍获得猪舍监控视频,建立了图像数据集,并进行了检测实验。实验结果表明,与直接利用YOLO v3模型相比,本文方法对头尾定位的精确率和召回率均有一定提高。本文方法对猪只头尾辨别精确率达到96.22%,与其他方法相比具有明显优势。展开更多
This paper explores the communicative acts deployed in covid-19 vaccination-related pictorials circulated on digital media platforms.Seven internet images were purposively sampled with a view to exploring their commun...This paper explores the communicative acts deployed in covid-19 vaccination-related pictorials circulated on digital media platforms.Seven internet images were purposively sampled with a view to exploring their communicative functions as well as their generic structure.The data,which were culled from the websites of the World Health Organisation,Centre for Disease Control,Pan American Health Organisation and Facebook,were subjected to qualitative analysis.The study deployed van Leeuwen’s Multimodal Discourse Analysis and Yuen’s Generic Structure Potential as theoretical anchor.The multimodal communicative acts are deployed for instructive,illustrative,informative,persuasive,inviting and advisory purposes.Categories such as Lead,Emblem,Announcement and Enhancer are compulsory in the data while Display,Tag and Call-and-Visit Information are non-compulsory elements.This can be catalogued as:‘Lead^(Display)^Emblem^(Announcement)^(Enhancer)^(Tag)^(Call-and-Visit Information)’.The study contends that the various semiotic resources deployed in the internet-circulated covid-19 images are used not only for informative and other communicative purposes but also to evoke attitudinal change towards and encourage widespread acceptance of the covid-19 vaccines.展开更多
文摘在利用视频监控技术对群养猪只进行自动行为监测时,对猪只准确定位并辨别其头尾位置对提高监测水平至关重要,基于此提出一种基于YOLO v3(You only look once v3)模型与图结构模型(Pictorial structure models)的猪只头尾辨别方法。首先,利用基于深度卷积神经网络的YOLO v3目标检测模型,训练猪只整体及其头部和尾部3类目标的检测器,从而在输入图像中获得猪只整体及头尾部所有的检测结果;然后,引入图结构模型,描述猪只的头尾结构特征,对每个猪只整体检测矩形框内的头尾部位组合计算匹配得分,选择最优的部位组合方式;对部分部位漏检的情况,采取阈值分割与前景椭圆拟合的方法,根据椭圆长轴推理出缺失部位。在实际猪场环境下,通过俯拍获得猪舍监控视频,建立了图像数据集,并进行了检测实验。实验结果表明,与直接利用YOLO v3模型相比,本文方法对头尾定位的精确率和召回率均有一定提高。本文方法对猪只头尾辨别精确率达到96.22%,与其他方法相比具有明显优势。
文摘This paper explores the communicative acts deployed in covid-19 vaccination-related pictorials circulated on digital media platforms.Seven internet images were purposively sampled with a view to exploring their communicative functions as well as their generic structure.The data,which were culled from the websites of the World Health Organisation,Centre for Disease Control,Pan American Health Organisation and Facebook,were subjected to qualitative analysis.The study deployed van Leeuwen’s Multimodal Discourse Analysis and Yuen’s Generic Structure Potential as theoretical anchor.The multimodal communicative acts are deployed for instructive,illustrative,informative,persuasive,inviting and advisory purposes.Categories such as Lead,Emblem,Announcement and Enhancer are compulsory in the data while Display,Tag and Call-and-Visit Information are non-compulsory elements.This can be catalogued as:‘Lead^(Display)^Emblem^(Announcement)^(Enhancer)^(Tag)^(Call-and-Visit Information)’.The study contends that the various semiotic resources deployed in the internet-circulated covid-19 images are used not only for informative and other communicative purposes but also to evoke attitudinal change towards and encourage widespread acceptance of the covid-19 vaccines.
