Duan Weijun, Wang Fang, Li Honghui, Wang Buyu, Wang Yuan, Fu Xueliang
College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
National Center of Technology Innovation for Dairy-Breeding and Production Research Subcenter, Hohhot 010018, China.
Sensors (Basel). 2025 Aug 8;25(16):4910. doi: 10.3390/s25164910.
Individual identification of Holstein cattle is crucial for the intelligent management of farms. The existing closed-set identification models are inadequate for breeding scenarios where new individuals continually join, and they are highly sensitive to obstructions and alterations in the cattle's appearance, such as back defacement. The current open-set identification methods exhibit low discriminatory stability for new individuals. These limitations significantly hinder the application and promotion of the model. To address these challenges, this paper proposes a prototype network-based incremental identification framework for Holstein cattle to achieve stable identification of new individuals under small sample conditions. Firstly, we design a feature extraction network, ResWTA, which integrates wavelet convolution with a spatial attention mechanism. This design enhances the model's response to low-level features by adjusting the convolutional receptive field, thereby improving its feature extraction capabilities. Secondly, we construct a few-shot augmented prototype network to bolster the framework's robustness for incremental identification. Lastly, we systematically evaluate the effects of various loss functions, prototype computation methods, and distance metrics on identification performance. The experimental results indicate that utilizing ResWTA as the feature extraction network achieves a top-1 accuracy of 97.43% and a top-5 accuracy of 99.54%. Furthermore, introducing the few-shot augmented prototype network enhances the top-1 accuracy by 4.77%. When combined with the Triplet loss function and the Manhattan distance metric, the identification accuracy of the framework can reach up to 94.33%. Notably, this combination reduces the incremental learning forgetfulness by 4.89% compared to the baseline model, while improving the average incremental accuracy by 2.4%. The proposed method not only facilitates incremental identification of Holstein cattle but also significantly bolsters the robustness of the identification process, thereby providing effective technical support for intelligent farm management.
荷斯坦奶牛的个体识别对于农场的智能化管理至关重要。现有的封闭集识别模型不足以应对新个体不断加入的育种场景，并且它们对奶牛外观的遮挡和变化（如背部毁损）高度敏感。当前的开放集识别方法对新个体的鉴别稳定性较低。这些限制显著阻碍了该模型的应用和推广。为应对这些挑战，本文提出了一种基于原型网络的荷斯坦奶牛增量识别框架，以在小样本条件下实现对新个体的稳定识别。首先，我们设计了一个特征提取网络ResWTA，它将小波卷积与空间注意力机制相结合。这种设计通过调整卷积感受野增强了模型对低级特征的响应，从而提高了其特征提取能力。其次，我们构建了一个少样本增强原型网络，以增强框架进行增量识别的鲁棒性。最后，我们系统地评估了各种损失函数、原型计算方法和距离度量对识别性能的影响。实验结果表明，使用ResWTA作为特征提取网络时，top-1准确率达到97.43%，top-5准确率达到99.54%。此外，引入少样本增强原型网络使top-1准确率提高了4.77%。当与三元组损失函数和曼哈顿距离度量相结合时，该框架的识别准确率可达94.33%。值得注意的是，与基线模型相比，这种组合将增量学习遗忘率降低了4.89%，同时将平均增量准确率提高了2.4%。所提出的方法不仅便于荷斯坦奶牛的增量识别，还显著增强了识别过程的鲁棒性，从而为智能农场管理提供了有效的技术支持。
