**作者：**O. Geffen1,2,3 , Y. Yitzhaky2, N. Barchilon1,3, S. Druyan3 and I. Halachmi1†
介绍背景，介绍生产环境，使用什么算法，达到什么效果
Manually counting hens in battery cages on large commercial poultry farms is a challenging task: time-consuming and often inaccurate. Therefore, the aim of this study was to develop a machine vision system that automatically counts the number of hens in battery cages. Automatically counting hens can help a regulatory agency or inspecting officer to estimate the number of living birds in a cage and, thus animal density, to ensure that they conform to government regulations or quality certification requirements. The test hen house was 87 m long, containing 37 battery cages stacked in 6-story high rows on both sides of the structure. Each cage housed 18 to 30 hens, for a total of approximately 11 000 laying hens. A feeder moves along the cages. A camera was installed on an arm connected to the feeder, which was specifically developed for this purpose. A wide-angle lens was used in order to frame an entire cage in the field of view. Detection and tracking algorithms were designed to detect hens in cages; the recorded videos were first processed using a convolutional neural network (CNN) object detection algorithm called Faster R-CNN, with an input of multi-angular view shifted images. After the initial detection, the hens’ relative location along the feeder was tracked and saved using a tracking algorithm. Information was added with every additional frame, as the camera arm moved along the cages. The algorithm count was compared with that made by a human observer (the ‘gold standard’). A validation dataset of about 2000 images achieved 89.6% accuracy at cage level, with a mean absolute error of 2.5 hens per cage. These results indicate that the model developed in this study is practicable for obtaining fairly good estimates of the number of laying hens in battery cages.
分为检测和跟踪两个网络
使用Faster R-CNN，Faster R-CNN有两个分支，分别是①RPN（region proposal network，区域建议网络），②classfier（分类器），主干网络使用的是ResNet101（残差网络101层）
检测算法的训练集：（287张图片用于训练，72张用于测试 ，8:2）
检测算法的主要流程：
文献中也提到了鸡易受惊吓的问题，所以将摄像头固定在喂食器上，然后跟着喂食器运动，拍摄时刻，一天喂5次，通过实验和观察，发现第5次进行拍摄时效果最好，因为第5次的时候，最多鸡出来吃，即遮挡最不严重的时候