Enhancing YOLOv8 with deformable attention for efficient and accurate pig detection

Pig detection technology is of paramount importance in modern livestock farming, especially within large-scale breeding environments. It not only significantly enhances breeding efficiency by optimizing feeding strategies through real-time monitoring of the pigs' health and growth status but also substantially reduces the risk of disease transmission, yielding notable economic benefits for farmers. With the livestock industry's shift towards automation and intelligence, the demand for more accurate and efficient pig detection technologies has been growing. Among the technologies employed, the YOLO series of models are frequently utilized for pig detection. However, the natural variety in the postures and forms of pigs can lead to recognition issues with some postures using standard models. Therefore, this paper introduces a Deformable Attention module to the YOLOv8 model, which not only captures more global information but also effectively addresses the issue of target deformability. By adaptively adjusting the shape of the convolutional kernels, the module enhances the capture of target features. Experimental results show that the improved model detects pigs more accurately than the original YOLOv8n model and achieves a 3.2% increase in the mAP^50-95 metric.