PDF(Pubmed)
Abstract：
This study underscores the paramount importance of facial expressions in pigs, serving as a sophisticated mode of communication to gauge their emotions, physical well-being, and intentions. Given the inherent challenges in deciphering such expressions due to pigs\' rudimentary facial muscle structure, we introduced an avant-garde pig facial expression recognition model named CReToNeXt-YOLOv5. The proposed model encompasses several refinements tailored for heightened accuracy and adeptness in detection. Primarily, the transition from the CIOU to the EIOU loss function optimized the training dynamics, leading to precision-driven regression outcomes. Furthermore, the incorporation of the Coordinate Attention mechanism accentuated the model\'s sensitivity to intricate expression features. A significant innovation was the integration of the CReToNeXt module, fortifying the model\'s prowess in discerning nuanced expressions. Efficacy trials revealed that CReToNeXt-YOLOv5 clinched a mean average precision (mAP) of 89.4%, marking a substantial enhancement by 6.7% relative to the foundational YOLOv5. Crucially, this advancement holds profound implications for animal welfare monitoring and research, as our findings underscore the model\'s capacity to revolutionize the accuracy of pig facial expression recognition, paving the way for more humane and informed livestock management practices.
摘要：
这项研究强调了猪面部表情的重要性，作为一种复杂的交流方式来衡量他们的情绪，身体健康，和意图。鉴于破译这种表情的内在挑战，由于猪的基本面部肌肉结构，我们介绍了一种前卫的猪面部表情识别模型，命名为CReToNeXt-YOLOv5。所提出的模型包括为提高检测的准确性和熟练度而量身定制的几种改进。首先,从CIOU到EIOU损失函数的过渡优化了训练动力学，导致精度驱动的回归结果。此外，协调注意力机制的结合增强了模型对复杂表达特征的敏感性。一项重大创新是集成了CReToNeXt模块，加强模型在辨别细致入微的表达方面的能力。功效试验显示，CReToNeXt-YOLOv5的平均精度（mAP）为89.4％，相对于基础YOLOv5，显著提高了6.7%。至关重要的是，这一进步对动物福利监测和研究具有深远的意义，正如我们的发现强调了该模型彻底改变猪面部表情识别准确性的能力，为更人性化和知情的牲畜管理实践铺平道路。
