Abstract
Accurately recognizing and assessing pain in sheep is key to discern animal health and mitigating harmful situations. However, such accuracy is limited by the ability to manage automatic monitoring of pain in those animals. Facial expression scoring is a widely used and useful method to evaluate pain in both humans and other living beings. Researchers also analyzed the facial expressions of sheep to assess their health state and concluded that facial landmark detection and pain level prediction are essential. For this purpose, we propose a novel weighted graph neural network (WGNN) model to link sheep's detected facial landmarks and define pain levels. Furthermore, we propose a new sheep facial landmarks dataset that adheres to the parameters of the Sheep Facial Expression Scale (SPFES). Currently, there is no comprehensive performance benchmark that specifically evaluates the use of graph neural networks (GNNs) on sheep facial landmark data to detect and measure pain levels. The YOLOv8n detector architecture achieves a mean average precision (mAP) of 59.30% with the sheep facial landmarks dataset, among seven other detection models. The WGNN framework has an accuracy of 92.71% for tracking multiple facial parts expressions with the YOLOv8n lightweight on-board device deployment-capable model.
Abstract (translated)
准确地识别和评估羊的疼痛对于判断动物健康状况以及减轻有害情况至关重要。然而，这种准确性受限于自动监测这些动物疼痛的能力。面部表情评分是一种广泛使用且有效的评估人类及其他生物疼痛的方法。研究人员还分析了羊的面部表情来评估其健康状态，并得出结论：面部特征检测及疼痛水平预测是至关重要的。为此，我们提出了一种新颖的加权图神经网络（WGNN）模型，用于连接所检测到的羊面部特征并定义疼痛级别。此外，我们提出了一个新的羊面部标志数据集，该数据集遵循《羊面部表情量表》(Sheep Pain Facial Expression Scale, SPFES) 的参数标准。 目前还没有一个全面的性能基准专门评估图神经网络（GNNs）在检测和测量羊面部特征数据疼痛水平方面的应用。YOLOv8n检测器架构在包含七个其他检测模型在内的羊面部标志数据集中，达到了平均精度均值 (mAP) 为59.30% 的成绩。使用 YOLOv8n 轻量级车载设备部署模型时，WGNN 框架对于跟踪多个面部部分表情的准确率为92.71%。
URL
https://arxiv.org/abs/2506.01468
