YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union

SIMPLE SUMMARY: We propose a new model, YOLOv5-SA-FC, for efficient pig population detection and counting in intelligent breeding. Traditional manual methods are slow and inaccurate. Our model incorporates shuffle attention (SA) and Focal-CIoU (FC) for an improved performance. SA enhances feature extraction without adding parameters, and FC reduces the sample imbalance impact. Our experiments show that YOLOv5-SA-FC achieves a 93.8% mean average precision (mAP) and 95.6% count accuracy, outperforming other methods by 10.2% and 15.8% in pig detection and counting. This validates its effectiveness in intelligent pig breeding. ABSTRACT: The efficient detection and counting of pig populations is critical for the promotion of intelligent breeding. Traditional methods for pig detection and counting mainly rely on manual labor, which is either time-consuming and inefficient or lacks sufficient detection accuracy. To address these issues, a novel model for pig detection and counting based on YOLOv5 enhanced with shuffle attention (SA) and Focal-CIoU (FC) is proposed in this paper, which we call YOLOv5-SA-FC. The SA attention module in this model enables multi-channel information fusion with almost no additional parameters, enhancing the richness and robustness of feature extraction. Furthermore, the Focal-CIoU localization loss helps to reduce the impact of sample imbalance on the detection results, improving the overall performance of the model. From the experimental results, the proposed YOLOv5-SA-FC model achieved a mean average precision (mAP) and count accuracy of 93.8% and 95.6%, outperforming other methods in terms of pig detection and counting by 10.2% and 15.8%, respectively. These findings verify the effectiveness of the proposed YOLOv5-SA-FC model for pig population detection and counting in the context of intelligent pig breeding.