This paper proposes and evaluates, for the first time, a top-down (dorsal view), depth-only deep learning system for accurately identifying individual cattle and provides associated code, datasets, and training weights for immediate reproducibility. An increase in herd size skews the cow-to-human ratio at the farm and makes the manual monitoring of individuals more challenging. Therefore, real-time cattle identification is essential for the farms and a crucial step towards precision livestock farming. Underpinned by our previous work, this paper introduces a deep-metric learning method for cattle identification using depth data from an off-the-shelf 3D camera. The method relies on CNN and MLP backbones that learn well-generalised embedding spaces from the body shape to differentiate individuals -- requiring neither species-specific coat patterns nor close-up muzzle prints for operation. The network embeddings are clustered using a simple algorithm such as $k$-NN for highly accurate identification, thus eliminating the need to retrain the network for enrolling new individuals. We evaluate two backbone architectures, ResNet, as previously used to identify Holstein Friesians using RGB images, and PointNet, which is specialised to operate on 3D point clouds. We also present CowDepth2023, a new dataset containing 21,490 synchronised colour-depth image pairs of 99 cows, to evaluate the backbones. Both ResNet and PointNet architectures, which consume depth maps and point clouds, respectively, led to high accuracy that is on par with the coat pattern-based backbone.

针对牛只的精准识别和精细畜牧的需要，本文首次提出和评估了一种自上而下（背部视角）的深度学习系统，该系统可使用深度数据准确识别个体牛只，为进一步研究提供了代码、数据集和训练权重。研究采用了基于卷积神经网络和多层感知器的背骨架，并通过生成嵌入空间进行聚类以实现高精度识别，无需重新训练网络。通过对ResNet和PointNet两种不同的背骨架进行评估，结果表明使用深度图和点云分别作为输入的模型均能获得与基于皮毛花纹的背骨架相当的高准确性。

基于深度数据和深度度量学习的普适性牛标识