Abstract：It is difficult to obtain the video image data of air maneuvering military targets under the complex actual combat background. The lack of training data limit the performance of target detection and recognition algorithms based on deep learning. Thus, this paper proposes an improved algorithm based on the deep convolutional generative adversarial networks (DCGAN). The enhanced algorithm could generate air maneuvering target data by injecting random vectors. Besides, the discriminator in this paper uses an improved Wasserstein distance measurement to generate the data distribution of the sample and the real sample, thereby optimizing the loss function and improving the stability of the DCGAN model training process and the quality of the generated image. Experimental results show that the improved DCGAN image generation algorithm can generate air maneuvering military targets images in various actual scenarios. After the improvement, the model training process is stable, the loss function fluctuations are significantly reduced, and the generated images are more realistic. The FID and IS scores in 32×32 resolution images have increased by 9.4% and 7.6% respectively. The FID and IS scores in 64×64 resolution images have increased by 5.9% and 4.8% respectively.
祁生勇, 臧月进, 吕国云, 杜明. 基于生成对抗网络的空中目标图像生成算法研究[J]. 空天防御, 2021, 4(2): 67-73.
QI Shengyong, ZANG Yuejin, LYU Guoyun, DU Ming. Research on Air Target Image Generation Algorithm Based on Generative Adversarial Networks. Air & Space Defense, 2021, 4(2): 67-73.
