Infrared-visible cross-domain target recognition based on an improved CycleGAN

For current deep learning target recognition models, the performance significantly decreases when the training data and application data have different feature distributions. This issue becomes more severe, especially in low-resolution scenarios. This paper proposes an infrared-visible cross-domain target recognition method based on an improved CycleGAN to enhance the recognition accuracy of the classifier trained with infrared images for visible images under low-resolution conditions. Firstly, we construct an image enhancement module aimed at enhancing the details and overall quality of low-resolution images. Secondly, the CycleGAN network model is improved with a dual-discriminator adversarial structure. The improved CycleGAN is utilized to transform visible images into infrared images and reduce the feature gap between the two image types. Finally, we conduct cross-domain target recognition experiments using the DSAIC dataset and compare our method with several other approaches. The results demonstrate that the proposed method effectively enhances the accuracy and generalization capability of the infrared classifier for visible light targets, while retaining high accuracy and generalization capability for infrared targets.