부산대학교 도서관

Ear biting is a welfare challenge in commercial pig farming. Pigs sustain injuries at the bite site paving the way for bacterial infections. Early detection and management of this behavior are important to enhance animal health and welfare, increase productivity, and minimize inputs from medication. Pig management using physical observation is impractical because of the scale of modern pig production systems. The same applies to the manual analysis of videos captured from pigsty. Therefore, a method of automated detection is desirable. In this study, we introduce an automatic detection pipeline based on deep learning for the quantification of ear biting outbreaks. Two state-of-the-art detection networks, YOLOv4 and YOLOv7, were trained to localize the regions of ear biting. The detected regions were tracked over multiple video frames using DeepSORT and Centroid tracking algorithms. Tracking provided the association between detected instances in video frames, enabling the computation of the frequency and duration of occurrence. The frequency and duration of ear biting were expressed as the cumulative performance of each group of pigs. The pipeline was evaluated using two datasets from experimental and commercial farms with diverse management and monitoring settings. The detection networks achieved comparable average precision values of 98% & 97.5% and 85.6% & 80.9% on the respective datasets. The tracking algorithms produced 14% and 34% False-Alarm rates, respectively. The results show that automated detection and tracking of ear biting is possible. Subsequently, we applied our method to videos in which pigs were managed in a manner that was expected to affect the frequency of ear biting to different degrees. This method can be used as the basis of an early warning system for the detection of ear-biting in commercial farms.