Data augmentation method of infrared ship target based on spatial association

In order to solve the problems that lacking of infrared images for ship target and the difficulty of acquiring them, an improved infrared image data augmentation method with geometric space and feature space association for ship target is proposed based on the image geometry changes and feature fitting method with generative adversarial network. Firstly, the IR image of ship target was augmented by image transformation methods such as geometric transformation on geometric space, image hybridization and random erasure; Secondly, the pyramidal generative adversarial network (SinGAN) structure was improved according to the characteristics of the IR ship image, and the In-SE-Net inter-channel attention mechanism module was introduced in the generator to enhance the small sensory field feature representation, making it more suitable for the IR ship target; Finally, at the data set level, geometric data transformation based on geometric space and generative adversarial network based on feature space were combined to complete the data augmentation of the original dataset. Object detection algorithms such as YOLOv3, SSD, R-FCN and Faster R-CNN were used as benchmark models to carry out in infrared ship image data augmentation experiments. The average accuracy (mAP) of object detection were all improved by about 10% trained on the augmented data, which verified the feasibility of the proposed method on small-sample infrared ship image data augmentation. It also provides a data basis for improving object detection algorithm carried of infrared ship.