With the development of infrared detection technology and the improvement of military remote sensing needs, infrared object detection networks with low false alarms and high detection accuracy have been a research focus. However, due to the lack of texture information, the false detection rate of infrared object detection is high, resulting in reduced object detection accuracy. To solve these problems, we propose an infrared object detection network named Dual-YOLO, which integrates visible image features. To ensure the speed of model detection, we choose the You Only Look Once v7 (YOLOv7) as the basic framework and design the infrared and visible images dual feature extraction channels. In addition, we develop attention fusion and fusion shuffle modules to reduce the detection error caused by redundant fusion feature information. Moreover, we introduce the Inception and SE modules to enhance the complementary characteristics of infrared and visible images. Furthermore, we design the fusion loss function to make the network converge fast during training. The experimental results show that the proposed Dual-YOLO network reaches 71.8% mean Average Precision (mAP) in the DroneVehicle remote sensing dataset and 73.2% mAP in the KAIST pedestrian dataset. The detection accuracy reaches 84.5% in the FLIR dataset. The proposed architecture is expected to be applied in the fields of military reconnaissance, unmanned driving, and public safety.

Ghost imaging is a novel imaging technique that utilizes the intensity correlation property of an optical field to retrieve information of the scene being measured. Due to the advantages of simple structure, high detection efficiency, etc., ghost imaging exhibits broad application prospects in the fields of space remote sensing, optical encryption transmission, medical imaging, and so on. At present, ghost imaging is gradually developing toward practicality, in which ghost imaging of moving targets is becoming a much-needed breakthrough link. At this stage, we can improve the imaging speed and improve the imaging quality to seek a more optimized ghost imaging scheme for moving targets. Based on the principle of moving target ghost imaging, this review summarizes and compares the existing methods for ghost imaging of moving targets. It also discusses the research direction and the technical challenges at the current stage to provide references for further promotion of the instantiation of ghost imaging applications.