Science and Innovation for Sustainable Poultry Production (WING), University of Veterinary Medicine Hannover, Foundation, 49377 Vechta, Germany.
Institute for Animal Hygiene, Animal Welfare and Animal Behavior, University of Veterinary Medicine Hannover, Foundation, 30173 Hannover, Germany.
Sensors (Basel). 2022 Jul 11;22(14):5188. doi: 10.3390/s22145188.
Injurious pecking against conspecifics is a serious problem in turkey husbandry. Bloody injuries act as a trigger mechanism to induce further pecking, and timely detection and intervention can prevent massive animal welfare impairments and costly losses. Thus, the overarching aim is to develop a camera-based system to monitor the flock and detect injuries using neural networks. In a preliminary study, images of turkeys were annotated by labelling potential injuries. These were used to train a network for injury detection. Here, we applied a keypoint detection model to provide more information on animal position and indicate injury location. Therefore, seven turkey keypoints were defined, and 244 images (showing 7660 birds) were manually annotated. Two state-of-the-art approaches for pose estimation were adjusted, and their results were compared. Subsequently, a better keypoint detection model (HRNet-W48) was combined with the segmentation model for injury detection. For example, individual injuries were classified using "near tail" or "near head" labels. Summarizing, the keypoint detection showed good results and could clearly differentiate between individual animals even in crowded situations.
同类相残是火鸡养殖中的一个严重问题。血腥的伤口会引发进一步的啄食行为，及时发现和干预可以防止动物福利受到严重损害和造成巨大损失。因此，我们的首要目标是开发一种基于摄像头的系统，利用神经网络来监测鸡群并检测伤口。在初步研究中，通过标记潜在的伤口来对火鸡的图像进行注释。然后使用这些图像来训练一个用于伤口检测的网络。在这里，我们应用了关键点检测模型来提供更多有关动物位置的信息，并指示伤口的位置。因此，我们定义了七个火鸡关键点，并对 244 张图像（显示 7660 只鸟）进行了手动注释。我们调整了两种最先进的姿势估计方法，并比较了它们的结果。随后，我们将一个更好的关键点检测模型（HRNet-W48）与用于伤口检测的分割模型相结合。例如，使用“靠近尾部”或“靠近头部”的标签来对个体伤口进行分类。总之，关键点检测的效果很好，即使在拥挤的情况下也能清楚地区分个体动物。
