RS Transformer: A Two-Stage Region Proposal Using Swin Transformer for Few-Shot Pest Detection in Automated Agricultural Monitoring Systems

Abstract

Agriculture is pivotal in national economies, with pest classification significantly influencing food quality and quantity. In recent years, pest classification methods based on deep learning have made progress. However, there are two problems with these methods. One is that there are few multi-scale pest detection algorithms, and they often lack effective global information integration and discriminative feature representation. The other is the lack of high-quality agricultural pest datasets, leading to insufficient training samples. To overcome these two limitations, we propose two methods called RS Transformer (a two-stage region proposal using Swin Transformer) and the Randomly Generated Stable Diffusion Dataset (RGSDD). Firstly, we found that the diffusion model can generate high-resolution images, so we developed a training strategy called the RGSDD, which was used to generate agricultural pest images and was mixed with real datasets for training. Secondly, RS Transformer uses Swin Transformer as the backbone to enhance the ability to extract global features, while reducing the computational burden of the previous Transformer. Finally, we added a region proposal network and ROI Align to form a two-stage training mode. The experimental results on the datasets show that RS Transformer has a better performance than the other models do. The RGSDD helps to improve the training accuracy of the model. Compared with methods of the same type, RS Transformer achieves up to 4.62% of improvement.