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.

在大型商业家禽养殖场的电池笼中手动计数母鸡是一项艰巨的任务：既费时又不准确。因此，本研究的目的是开发一种机器视觉系统，该系统可自动计算电池笼中母鸡的数量。自动对母鸡进行计数可以帮助管理机构或检查人员估算笼子中活禽的数量，从而估计动物的密度，以确保它们符合政府法规或质量认证要求。测试鸡舍长87 m，在结构的两侧装有37个电池笼，这些电池笼以6层高的行堆叠。每个笼子可容纳18到30头母鸡，总共约11000头产蛋鸡。送纸器沿着笼子移动。摄像机安装在连接到进纸器的手臂上，该摄像机是专门为此目的而开发的。为了在视场中框住整个笼子，使用了广角镜。检测和跟踪算法旨在检测笼中的母鸡。录制的视频首先使用卷积神经网络处理（CNN）对象检测算法，称为Faster R-CNN，具有多角度视图平移图像的输入。初步检测后，使用跟踪算法跟踪并保存母鸡沿喂料器的相对位置。当摄像机臂沿笼子移动时，信息会随每个其他帧一起添加。将算法计数与人工观察者的计数（“黄金标准”）进行比较。约2000张图像的验证数据集在笼级精度达到89.6％，平均绝对误差为每笼2.5只母鸡。这些结果表明，在本研究中开发的模型对于获得电池笼中蛋鸡数量的相当不错的估计是可行的。