用自注意力残差网络提升奶牛乳头健康评估准确率
A Self-attention Residual Convolutional Neural Network for Health Condition Classification of Cow Teat Images
- 结合残差连接与自注意力机制，增强模型对复杂图像的适应性
- 在真实场景下实现高精度乳头角化程度分类，提升评估效率
- 适合牧场与兽医用于快速、无创的奶牛健康筛查
牛奶是美国消费者的重要食品，奶牛乳头健康直接影响牛奶质量。传统上，兽医在挤奶过程中通过视觉检查乳头末端角化程度，但时间有限（通常仅数十秒），导致评估准确性受限。尽管卷积神经网络（CNN）已用于乳头健康评估，但仍面临环境复杂、乳头位置姿态多变、图像中难以定位乳头等挑战。为此，本文提出一种奶牛乳头自注意力残差卷积神经网络（CTSAR-CNN），融合残差连接与自注意力机制，通过数字图像自动分类乳头末端角化程度，辅助养殖场进行健康评估。实验结果表明，集成残差连接与自注意力机制后，模型准确率显著提升。该研究证明，CTSAR-CNN具备更强的适应性与实时性，可有效辅助兽医评估奶牛乳头健康，最终惠及乳制品行业。
原文摘要 · Abstract (English)
Milk is a highly important consumer for Americans and the health of the cows' teats directly affects the quality of the milk. Traditionally, veterinarians manually assessed teat health by visually inspecting teat-end hyperkeratosis during the milking process which is limited in time, usually only tens of seconds, and weakens the accuracy of the health assessment of cows' teats. Convolutional neural networks (CNNs) have been used for cows' teat-end health assessment. However, there are challenges in using CNNs for cows' teat-end health assessment, such as complex environments, changing positions and postures of cows' teats, and difficulty in identifying cows' teats from images. To address these challenges, this paper proposes a cows' teats self-attention residual convolutional neural network (CTSAR-CNN) model that combines residual connectivity and self-attention mechanisms to assist commercial farms in the health assessment of cows' teats by classifying the magnitude of teat-end hyperkeratosis using digital images. The results showed that upon integrating residual connectivity and self-attention mechanisms, the accuracy of CTSAR-CNN has been improved. This research illustrates that CTSAR-CNN can be more adaptable and speedy to assist veterinarians in assessing the health of cows' teats and ultimately benefit the dairy industry.
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