In order to deal with the difficulty and excessive cost in acquiring infrared data of ground non cooperative special vehicle targets,and solve the problems of over fitting and poor generalization ability for small sample data sets,a data augmented method based on geometric feature space transformation is proposed for ground vehicle infrared data.Firstly,the original ground vehicle infrared data set is constructed by high definition infrared equipment.Then,in conjunction with the geometric feature space transformation method,the reconstruction mechanism of the SinGAN neural network is leveraged to augment the infrared data sets and build the Infrared VOC data sets.Finally,a variety of the target detection models is employed to validate the performance of the augmented infrared data sets.The effectiveness of the geometric feature space transformation for data augmentation is verified by several benchmark test cases,which provides a new method for ground non cooperative special vehicle infrared data augmentation.