Leveraging Thermal Infrared Imaging for Pig Ear Detection and Temperature Distribution Research: The TIRPigEar Dataset and New Deep Learning Model Performances

The stable physiological structure and rich vascular network of pig ears more easily form distinct thermal characteristics, representing pig body temperature. Using thermal infrared imaging to identify pig ears offers a feasible approach to analyzing individual pig health 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 health inspection robot equipped with an infrared thermal imaging device, with post-processing conducted via manual annotation. By labeling pig ear targets 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 more accurate and timely pig-ear temperature analysis.