基于CycleGAN的月表图像数据增强方法

In this paper, a method of lunar surface priori image data enhancement based on adversarial neural network is proposed to address the problem of difficulty in acquiring a priori image information on the lunar surface. Based on the acquisition of a small amount of lunar surface images and obstacle background segmentation maps, the lunar surface image data enhancement architecture based on the adversarial neural network is constructed, and the new obstacle background segmentation maps are used to match the lunar surface images and expand the lunar surface priori image data, which can be used for the design and verification of obstacle detection algorithms in lunar exploration. Simulation results prove that the lunar surface images generated by the proposed method are close to the real captured images, and the image data is enhanced by the data to obtain obvious improvement of the obstacle detection results, which proves the effectiveness of the proposed method.