A Study on Small Pest Detection Based on a CascadeR-CNN-Swin Model

This study aims to detect and prevent greening disease in citrus trees using a deep neural network. The process of collecting data on citrus greening disease is very difficult because the vector pests are too small. In this paper, since the amount of data collected for deep learning is insufficient, we intend to use the efficient feature extraction function of the neural network based on the Transformer algorithm. We want to use the Cascade Region-based Convolutional Neural Networks (Cascade R-CNN) Swin model, which is a mixture of the transformer model and Cascade R-CNN model to detect greening disease occurring in citrus. In this paper, we try to improve model safety by establishing a linear relationship between samples using Mixup and Cutmix algorithms, which are image processing-based data augmentation techniques. In addition, by using the ImageNet dataset, transfer learning, and stochastic weight averaging (SWA) methods, more accuracy can be obtained. This study compared the Faster Region-based Convolutional Neural Networks Residual Network101 (Faster R-CNN ResNet101) model, Cascade Region-based Convolutional Neural Networks Residual Network101 (Cascade R-CNN-ResNet101) model, and Cascade R-CNN Swin Model. As a result, the Faster R-CNN ResNet101 model came out as Average Precision (AP) (Intersection over Union (IoU)=0.5): 88.2%, AP(IoU = 0.75): 62.8%, Recall: 68.2%, and the Cascade R-CNN ResNet101 model was AP(IoU = 0.5): 91.5%, AP (IoU = 0.75): 67.2%, Recall: 73.1%. Alternatively, the Cascade R-CNN Swin Model showed AP (IoU = 0.5): 94.9%, AP (IoU = 0.75): 79.8% and Recall: 76.5%. Thus, the Cascade R-CNN Swin Model showed the best results for detecting citrus greening disease.