Convolutional Neural Networks (CNNs) have drawn researchers' attention to identifying cattle using muzzle images. However, CNNs often fail to capture long-range dependencies within the complex patterns of the muzzle. The transformers handle these challenges. This inspired us to fuse the strengths of CNNs and transformers in muzzle-based cattle identification. Addition and concatenation have been the most commonly used techniques for feature fusion. However, addition fails to preserve discriminative information, while concatenation results in an increase in dimensionality. Both methods are simple operations and cannot discover the relationships or interactions between fusing features. This research aims to overcome the issues faced by addition and concatenation. This research introduces a novel approach called Multi-Head Attention Feature Fusion (MHAFF) for the first time in cattle identification. MHAFF captures relations between the different types of fusing features while preserving their originality. The experiments show that MHAFF outperformed addition and concatenation techniques and the existing cattle identification methods in accuracy on two publicly available cattle datasets. MHAFF demonstrates excellent performance and quickly converges to achieve optimum accuracy of 99.88% and 99.52% in two cattle datasets simultaneously.

本研究针对卷积神经网络在牛只识别中未能有效捕捉长程依赖性的问题，提出了一种新方法——多头注意力特征融合（MHAFF）。该方法首次将CNN和Transformer的优势结合在一起，能够在保持特征原始性的同时捕获不同特征之间的关系。本研究的实验结果表明，MHAFF在两个公开牛只数据集中，准确率达到了99.88%和99.52%，优于传统的特征融合方法及现有牛只识别技术。

多头注意力特征融合：用于牛只识别的CNN与Transformer的结合