A deep learning-based detection method for pig body temperature using infrared thermography

Abstract

Body temperature is one of the important indicators that reflect the health of pigs. The traditional rectal temperature measurement is inconvenient and time-consuming, and also easy to cause stress responses to pigs. Infrared Thermography (ITG) was supposed to have potentials to realize non-invasive and rapid temperature detection for pigs in intensive pig farming. In this paper, an automatic temperature detection method base on ITG was developed. First, temperatures on six regions on pig body surface (i.e., forehead (FH), eyes, nose, ear root (ET), back and anus) were measured by ITG for the region of interest (ROI) selection; Then, an improved model of YOLOv5s-BiFPN was developed for automatic ROI detection and temperature extraction. A dataset with 2797 images that collected from sixteen pigs for 30 days was used for model development. It was shown that temperatures on FH and ET were supposed to be the ROI for ITG temperature detection and because of their strong correlations with the rectal temperature among the six parts on pig body surface. Also, the maximum temperature (MaxT) on FH and ET could reflect pig’s body temperature variations the best. The proposed model of YOLOv5s-BiFPN achieved optimal performances (e.g., the mAP was 96.36%, outperformance was 20 MB, target detection speed was up to 100 frame/s), and the mAPs were increased by 4.85%, 4.38%, 1.60%, 1.56%, 31.52%, and 22.42% compared with models of CenterNet, Faster R-CNN, YOLOv4, YOLOv5s, Nanodet and YOLOv5n, respectively. Therefore, it is a feasible way for pig body temperature automatic detection and facilitates early staged disease warning and environmental control.