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Leveraging Thermal Infrared Imaging for Pig Ear Detection Research: The TIRPigEar Dataset and Performances of Deep Learning Models

文献类型： 外文期刊

作者： Ma, Weihong 1 ; Wang, Xingmeng 2 ; Yang, Simon X. 3 ; Song, Lepeng 2 ; Li, Qifeng 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Chongqing Univ Sci & Technol, Sch Elect & Elect Engn, Chongqing 401331, Peoples R China

3.Univ Guelph, Sch Engn, Adv Robot & Intelligent Syst Lab, Guelph, ON N1G 2W1, Canada

关键词： thermal infrared imaging; pig state monitoring; deep learning for object detection; precision livestock farming

期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2025 年 15 卷 1 期

页码：

收录情况： SCI

摘要： The stable physiological structure and rich vascular network of pig ears contribute to distinct thermal characteristics, which can reflect temperature variations. While the temperature of the pig ear does not directly represent core body temperature due to the ear's role in thermoregulation, thermal infrared imaging offers a feasible approach to analyzing individual pig status. Based on this background, a dataset comprising 23,189 thermal infrared images of pig ears (TIRPigEar) was established. The TIRPigEar dataset was obtained through a pig house inspection robot equipped with an infrared thermal imaging device, with post-processing conducted via manual annotation. By labeling pig ears within these images, a total of 69,567 labeled files were generated, which can be directly used for training pig ear detection models and enabling the analysis of pig temperature information by integrating the corresponding thermal imaging data. To validate the dataset's utility, it was evaluated across various object detection algorithms. Experimental results show that the dataset achieves the highest precision, recall, and mAP50 on the YOLOv9m model, reaching 97.35%, 98.1%, and 98.6%, respectively. Overall, the TIRPigEar dataset demonstrates optimal performance when applied to the YOLOv9m algorithm. Utilizing thermal infrared imaging technology to detect pig ear information provides a non-contact, rapid, and effective method. Establishing the TIRPigEar dataset is highly significant, as it allows for a valuable resource for AI and precision livestock farming researchers to validate and improve their algorithms. This dataset will support many researchers in advancing precision livestock farming by enabling an efficient way for pig ear temperature analysis.

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