REGAN-BS-YOLOv8: A Novel Multi-Scale Storage Grain Pest Detection Model Established by Integrating Real-ESRGAN and Swin Transformer for YOLOv8

Accurate detection of grain pests is important for ensuring food security. The existing detection algorithms still face challenges such as low precision, high false positives, and missed detection when identifying storage pests due to the small size, complex backgrounds, and limited data availability. For this reason, this paper proposes a solution jointly driven by generative AI and analytical AI, named REGAN-BS-YOLOv8. Specifically, on the one hand, an image-generative AI method of Real_ESRGAN and the mosaic data augmentation method is introduced to improve the quality, resolution and number of grain pest images. On the other hand, a novel recognition algorithm is proposed by introducing Swin Transformer and BiFPN to YOLOv8n to obtain better perception ability, positioning accuracy, detail retention and edge clarity, improving the recognition accuracy, robustness, and generalization of stored-grain pest and the anti-interference capability in complex environments. In addition, a new dataset including 5818 images of nine types of grain pests at various scales and under diverse backgrounds was collected in this study. The experimental results show that the mAP@0.5, Precision and Recall are 97.7%, 96.2% and 95.4% respectively using the method proposed in this study, outperforming the other nine models. The performance of the model we proposed is excellent for different types of storage pests under different backgrounds, and the model identifies grain pests of different sizes in different environments with high precision and provides an important theoretical basis for the management of grain silos.