An Infrared Sequence Image Generating Method for Target Detection and Tracking - Zhijian, Huang; Bingwei, Hui; Shujin, Sun (Frontiers in Computational Neuroscience, 2022)
Training infrared target detection and tracking models based on deep learning requires a large number of infrared sequence images. The cost of acquisition real infrared target sequence images is high, while conventional simulation methods lack authenticity. This paper proposes a novel infrared data simulation method that combines real infrared images and simulated 3D infrared targets. Firstly, it stitches real infrared images into a panoramic image which is used as background. Then, the infrared characteristics of 3D aircraft are simulated on the tail nozzle, skin, and tail flame, which are used as targets. Finally, the background and targets are fused based on Unity3D, where the aircraft trajectory and attitude can be edited freely to generate rich multi-target infrared data. The experimental results show that the simulated image is not only visually similar to the real infrared image but also consistent with the real infrared image in terms of the performance of target detection algorithms.The method can provide training and testing samples for deep learning models for infrared target detection and tracking.