An Infrared Sequence Image Generating Method for Target Detection and Tracking

Abstract

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.

A neural learning approach for simultaneous object detection and grasp detection in cluttered scenes

“…These methods are easy to understand but often suffer efficiency problems due to 3D operations. Motivated by the superior performance of deep learning technology on detection or segmentation tasks (Cheon et al, 2022 ; Huang et al, 2022 ; Khan et al, 2022 ), image-based deep models have become popular for grasp detection (Chu et al, 2018 ; Zhang et al, 2019 ; Dong et al, 2021 ; Yu et al, 2022a ). These methods often use a rectangle representation g = ( x, y, h, w , θ), where ( x, y ) is the center pixel location of a grasp candidate, ( h, w ) are height and width of the gripper, and θ is the rotation of the gripper.…”