Recognition and segmentation of individual pigs based on Swin Transformer

Recognition of individual pigs is critical to the monitoring of pig body size and physiological health status in large-scale pig farms. In this study, deep learning methods were introduced in the intelligent recognition and segmentation of individual replacement pigs, which can realize the non-contact surveillance of each pig. Swin Transformer was used for the recognition and segmentation of individual pigs based on the surveillance data, and different models were compared to find the model with the fastest training speed and most accurate results. Finally, a recognition accuracy of 93.0% and segmentation accuracy of 86.9% for individual pigs were achieved with the surveillance video images of pigs based on Swin Transformer. Even in some complex scenarios such as overlapping, occlusion, and deformation, the method still exhibited excellent recognition performance for replacement pigs. Importantly, this method can greatly save labor as well as help intelligent and unmanned pig production and facilitate the modernization of pig industry.