Deformable Two-Stage Generative Adversarial Network Augmentation for Drosophila Image Recognition in Complex Environments

Drosophila pose a significant threat to crops, and early detection and identification of Drosophila are crucial. However, the lack of complex Drosophila datasets makes it challenging to use deep learning models in practical scenarios, and traditional data augmentation methods struggle to ensure the quality and effectiveness of generated images. To address the issue of decreased model performance due to dataset scarcity, we propose a deformable two-stage generative adversarial network (DTS-GAN) approach. By synthesizing Drosophila images to augment the original dataset, the accuracy of the recognition model based on the ResNet50 backbone is significantly improved. This approach enhances the quality and diversity of synthesized images and solves the difficulty of Drosophila identification in complex backgrounds. Experimental results demonstrate that using the proposed DTS-GAN to generate Drosophila images effectively improves the accuracy of the recognition model with an increase from 80.84% to 86.52% compared to the original precision.