用图神经网络分析羊脸表情，精准识别疼痛状态。
Sheep Facial Pain Assessment Under Weighted Graph Neural Networks
- 构建加权图神经网络，关联羊脸关键点预测疼痛
- 模型在轻量设备上实现92.71%的多部位表情追踪准确率
- 首次提供符合SPFES标准的羊脸地标数据集
准确识别和评估羊的疼痛对判断动物健康、减少伤害至关重要。然而，自动监测能力限制了精度提升。面部表情评分是人类及其他生物评估疼痛的常用方法，研究发现羊的面部关键点检测与疼痛等级预测尤为关键。为此，本文提出一种新型加权图神经网络（WGNN）模型，用于连接羊的面部关键点并定义疼痛水平。同时，构建了一个符合羊面部表情量表（SPFES）标准的新羊脸关键点数据集。目前尚无针对羊脸关键点数据使用图神经网络进行疼痛检测与评估的系统性性能基准。在七种检测模型中，YOLOv8n架构在该数据集上达到59.30%的平均精度（mAP）。WGNN框架在轻量级边缘设备部署模型下，实现了92.71%的多部位表情追踪准确率。
原文摘要 · Abstract (English)
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.
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