Automatic body temperature detection of group-housed piglets based on infrared and visible image fusion

Rapid and accurate measurement of body temperature is essential for early disease detection, as it is a key indicator of piglet health. Infrared thermography (IRT) is a widely used, convenient, non-intrusive, and efficient non-contact temperature measurement technology. However, the activities and clustering of group-housed piglets make it challenging to measure the individual body temperature using IRT. This study proposes a method for detecting body temperature in group-housed piglets using infrared-visible image fusion. The infrared and visible images were automatically captured by cameras mounted on a robot. An improved YOLOv8-PT model was proposed to detect both piglets and their key body regions (ears, abdomen and hip) in visible images. Subsequently, the Oriented FAST and Rotated BRIEF (ORB) image registration method and the U2Fusion image fusion network were employed to extract temperatures from the detected body parts. Finally, a core body temperature (CBT) estimation model was developed, with actual rectal temperature serving as the gold standard. The temperatures of three body parts detected by infrared thermography were used to estimate CBT, and the maximum estimated temperature based on these body parts (EBT-Max) was selected as the final result. In the experiment, the YOLOv8-PT model achieved a [email protected] of 93.6 %, precision of 93.3 %, recall of 88.9 %, and F1 score of 91.05 %. The average detection time per image was 4.3 ms, enabling real-time detection. Additionally, the mean absolute errors (MAE) and correlation coefficient between EBT-Max and actual rectal temperature is 0.40 °C and 0.6939, respectively. Therefore, this method provides a feasible and efficient approach for group-housed piglets body temperature detection and offers a reference for the development of automated pig health monitoring systems.