PIG RECOGNITION BASED ON YOLOV8-EAPNET

With the advancement of intelligent farming technology, computer vision-based animal behavior recognition has become an important tool for improving production efficiency and animal welfare in modern farming management. To overcome the challenge of balancing computational efficiency and accuracy in existing behavior recognition systems, this study proposes an optimized model based on YOLOv8-EAPNet for accurately recognizing four main pig behaviors: standing, sitting, lateral lying, and prone lying. The framework adopts a multi-level lightweight design, incorporating three advanced technologies—C2f-ECA, SPPELAN, and Detect_AFPN—to enhance joint feature response, resolve spatial differences between sitting and lateral lying, and reconstruct semantics in occluded areas. This strengthens the model's robustness in complex farming environments and significantly improves the accuracy of pig behavior recognition. Validated on farm data, the model achieved an average precision improvement of 1.5% on a self-built dataset, with specific accuracy increases of 0.9% for standing, 1.7% for sitting, 3.0% for prone lying, and 0.3% for lateral lying. This technology provides an automated tool for early warning of limb injuries and respiratory diseases in pigs, promoting the upgrade of intelligent health management in the livestock industry and supporting the modernization of large-scale pig farming.