Advancing Precision Pig Behavior Recognition through Real-Time Detection Transformer

With the growing demand for sustainable animal products, intelligent pig farming has become key to improving the efficiency of livestock agriculture. This paper employs the advanced Real-Time Detection Transformer (RTDETR) algorithm, based on Transformer technology, aiming to enhance the accuracy of pig behavior recognition. To adapt to the variable challenges of real-world environments, we have applied various data augmentation techniques to the Edinburgh Pig Behavior Video Dataset, including random cropping, horizontal flipping, RandomBrightnessContrast, and perspective transformation. By training and comparing the RTDETR with the YOLOv5 model, we observed the exceptional performance of RTDETR in complex environments, achieving a mean Average Precision (mAP) of 0.82. This research not only provides a robust algorithmic foundation for intelligent pig farming but also emphasizes the significant advantages of the Transformer-based RTDETR algorithm in improving the accuracy of pig behavior recognition in challenging environments.