目的 动物个体身份识别一直是智慧畜牧业的主要难题之一，由于动物个体本身与人类在图像识别上需要的数据特征不同以及各个特征作为个体属性之间的关系不明确，对动物个体识别领域的研究较少，针对具有高相似度的奶山羊个体身份识别问题，提出了基于深度学习的高相似度的奶山羊识别方法。方法 采集了26只萨能奶山羊的全身图像，利用SSD(single shot MultiBox detection)网络进行数据集预处理，并随机选取1 040幅图像作为训练集，260幅图像作为测试集。其次采用ResNet18(residual neural network)预训练模型并进行迁移学习，最后联合三元组损失函数与交叉熵损失函数进行参数调整。研究表明，采用联合损失函数并结合Adam优化器算法时，可获得较好的识别效果。此外，在实验部分针对奶山羊的特征选取问题上，对奶山羊的羊脸区域与奶山羊的全身区域分别采用了三元组损失函数与孪生网络，验证了对奶山羊的识别仅靠羊脸区域的特征时准确率较低；此外，针对网络的训练，本文不仅通过YOLOv3(you only look once)以及孪生网络(siamese network)验证了奶山羊本身属于高相似度的数据集，而且针对奶山羊数据集分别采用三元组损失函数与交叉熵损失函数作为唯一的损失函数，并验证了该方法的有效性。结果 奶山羊识别的最高精准度为93.077%，相较于Triplet-Loss损失函数74.615%的准确率以及CrossEntropy-Loss 89.615%准确率有了较大提升。结论 本文提出的基于深度学习的高相似度的奶山羊识别方法不仅具有较高的准确率，而且在奶山羊个体身份识别方面具有极大的应用价值，有助于准确识别羊的身份，为相似度高的动物个体身份识别提供了思路。

Objective It is essential for the quick response in tracking information of animals for intelligent agriculture and animal husbandry nowadays. Individual identification of animals has been one of the challenging issues in real-time monitoring. Different from traditional methods with high harmfulness such as imprinting, our deep learning based method is adopted to implement image recognition for the several of animal and human as well as the unclear multi-features relationship. Method First, our computer vision method is demonstrated for individual recognition of dairy goat based on deep learning. The 26 goats’ pictures-oriented are acquired including the head and other parts. Fancy fancy principal components analysis (PCA) is adopted as the data expansion methods to expand the dataset. A sum of 1 040 goats’ images are randomly selected for training, and 260 images are used as independent test sets; single shot MultiBox detection (SSD) network based dataset preprocessing is initial to be required. Our demonstration uses the siamese network for preliminary learning. The network structure and learning rate optimization algorithm are employed to adjust the parameters but not suitable for individual identity classification. It verifies the goat itself in terms of the highly similar data set. The effect of whole goat image is better than single head image. This obtained result has been greatly improved from the training of original head to the whole body in the context of is the solo Triplet-Loss function. The original image input of the Triplet-Loss function is composed of three pictures. Because the dairy goat is proven to have high similarity of individual based on the siamese network, it is not required to conduct the data sets integration derived of the Triplet-Loss function as well as the set of different goats images is complicated based on manual method. Next, Triplet-Loss function in dataset has its potentials compared with the siamese network method. Our joint loss function and transfer learning model residual neural network(ResNet18) obtain the goat information in terms of deep network structure. Finally, the joint loss function takes Adam as the optimizer algorithm, our demonstration can get qualified recognition as the hard batch (difficult triplets) of Triplet-Loss function are not required in the context of Triplet-Loss function and CrossEntropy-Loss function and related parameters. In addition of goats, we use the Triplet-Loss function and siamese network to option the feature for the goat face region and the whole goat region verifies the features of the goat face region recognition is not good in terms of high accuracy rate. Our illustration is not only uses you only look once (YOLOv3) network and siamese network to identify goats, but also uses transfer learning model to learn. The siamese network verifies that the goat itself based on high similarity data set, and the Triplet-Loss function and CrossEntropy-Loss function are used as final loss function to verify the effectiveness of the method. Result The SSD network was used to preprocess the dataset. The demonstrated results illustrate that the accuracy can be improved from 86% to 93.077% by combining the joint loss function with Adam algorithm. When the joint loss function is used with Adam optimization algorithm as well as the joint loss function accounts for a certain proportion, other correlation can be realized by adjusting the parameters, the result will be obtain better recognition effect. Just compared with 74.615% of Triplet-Loss function and 89.615% of CrossEntropy-Loss function, the highest recognition accuracy is 93.077%. Conclusion Our higher recognition effect of goat analysis is based on the model of deep learning. These goats cannot just get more effective facial features recognition but can obtain higher accuracy derived of the whole goat body. Intelligent research of individual goat should be conducted based on the segmented attribute of each part of the goat body. Furthermore, our research can lower high labor costs issues based on deep learning model in terms of computer vision archives.