Infrared time-sensitive target data augmentation algorithm based on generative model

Currently, infrared time-sensitive target detection technology is widely used in military and civil applications such as air defense and early warning, maritime surveillance, and precision guidance, but some high-value target images are difficult and expensive to acquire. To address the problems such as the lack of infrared time-sensitive target image data and the lack of multi-scene multi-target data for training, this paper proposes an infrared time-sensitive target data enhancement algorithm based on a generative model, which is a two-stage model. Firstly, in the first stage, the visible images containing time-sensitive targets are converted to infrared images by a modal conversion model based on CUT networks. Then in the second stage a large number of random targets are generated from the converted IR images using an adversarial random sample generation model to achieve the data enhancement effect. The coordinate attention mechanism is also introduced into the generator module in the second stage, which effectively enhances the feature extraction capability of the network. Finally, modal conversion experiments and sample random generation experiments are conducted, and the results show the feasibility of the data enhancement method of generative model proposed in this paper in IR time-sensitive target data enhancement, which provides a strong data support for improving IR time-sensitive target detection algorithm.