Junhao Wang

Accurate recognition of pig behavior is important for animal health monitoring and precision livestock farming. However, fine-grained behavior recognition in group-housed pigs is challenging because of individual occlusion, large posture variation, and subtle visual differences among similar behaviors. Here, an improved YOLOv8n-based model, EPP-YOLOv8n, was developed for fine-grained pig behavior recognition. An efficient multi-scale attention module was inserted after the spatial pyramid pooling-fast module (SPPF) to enhance behavior-related feature extraction. A behavior-factorized asymmetric head (BFA-Head) was designed with scale-adaptive mixed convolution to reduce detection-head complexity, while posture- and interaction-assisted supervision was incorporated to guide fine-grained feature learning. A partial-channel mixed convolution module (P5-selective PLC-Mix) was deployed within the Backbone P5 C2f module to reduce redundant computation, followed by task-aware structured pruning with recovery training for further model compression. On a dataset comprising 2713 images and 30,695 annotated instances after training-set augmentation, EPP-YOLOv8n achieved 98.13% precision, 94.50% recall, and 89.07% mean average precision at intersection over union (IoU) thresholds from 0.50 to 0.95 (mAP@0.5:0.95), representing increases of 2.05, 1.52, and 1.64 percentage points, respectively, over YOLOv8n. Parameter count and billion floating-point operations (GFLOPs) were reduced by 14.59% and 9.70%, respectively. These results indicate that the proposed model can improve fine-grained behavior recognition while reducing model complexity, supporting automated behavior monitoring in group-housed pigs.