Data Augmentation Method of Military Objective Image Samples Based on Improved CycleGAN
To solve the problems of insufficient sample quantity and quality in military target image recognition training,the CycleGAN model is improved and an effective augmented method is proposed.According to the characteristics of military target samples,the generator structure and loss function structure of the model are modified to improve the stability of the model and the quality of generated images.The image recognition model is trained with the expanded sample data sets,it is found that the images generated by the proposed model can effectively improve the accuracy of the recognition model,the practicability and feasibility of the proposed method in expanding military target samples is proved.
data augmentationCycleGANmilitary objectiveimage generationimage classification
万方数据
维普
