基于深度神经网络的视觉乳房检测

Sukumar Katamreddy, P. Doody, J. Walsh, D. Riordan
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引用次数: 3

摘要

本文讨论了用于辅助正在进行的人工犊牛断奶系统的技术。其目的是通过阻止小牛在断奶阶段吮吸乳汁来人工断奶。所提出的项圈式微气动阻震系统正在进行中。为了阻止小牛,应该有一种机制来视觉上准确地检测乳房。一种可能的方法是用安装在项圈上的摄像系统从一定距离检测乳房,并预测哺乳事件。一个预先训练的深度神经网络模型嵌入到一个安装在项圈上的摄像头设备中,将检测乳房并预测哺乳事件,从而使威慑系统启动。选择这种检测乳房的方法是为了克服由小牛试图哺乳引起的项圈变化的振荡运动,并且它不依赖于可变的可见光条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual Udder Detection with Deep Neural Networks
This paper discusses the techniques used to aid the work in progress artificial calf weaning system. The aim is to artificially wean the calves by deterring them to suckle the milk during the weaning phase. The proposed collar mounted micro-pneumatic deterring system is the work in progress. To deter the calves there should be a mechanism to visually detect the udder accurately. One possible way to do this is by detecting the udder from a certain distance with a collar mounted camera system and predict the suckling event. A pre-trained deep neural network model embedded in a device with camera mounted to the collar will detect the udder and predicts the suckling event which enables the deterring system to actuate. This method of detecting udders has been chosen to overcome collar’s varying oscillatory movements caused by the calf in an attempt to suckle and it is independent on variable visible light conditions.
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