EVIT-YOLOv8: Construction and research on African Swine Fever facial expression recognition

Abstract

The threats of infectious diseases such as African Swine Fever, Swine Erysipelas, and Blue Ear Disease to the pig farming industry have been increasing year by year. Producers often face difficulties in diagnosis, leading to the misuse of measures and the spread of epidemics. Addressing this, the proposed EVIT-YOLOv8 model integrates the EViT module to surpass ViT limitations and incorporates the CBAM module for enhanced image feature representation. Employing the GIOU loss function ensures better precision in capturing facial expression features, yielding an impressive Mean Average Precision (mAP) of 86.6% in differentiation tasks. Specifically, in African Swine Fever facial expression recognition, the model achieves a remarkable Precision of 85.2%, outperforming YOLOv5, YOLOv7, and YOLOv8 models by 6%, 23.5%, and 7.3%, respectively. This provides pig producers with a precise diagnostic tool, mitigating the risk of epidemic spread due to misdiagnosis and facilitating effective prevention and control of infectious diseases.