Visible-to- infrared image translation is an important way to enrich infrared data. However, the reliability of the data generated by image translation in downstream tasks has always been controversial. This paper proposes a method that integrates visible-to- infrared image translation tasks and multi-modal template matching tasks. The image generation network is based on a generative adversarial networks (GANs), and network training is supervised by L1 loss, GANs loss, and match loss, where the matching loss includes normalized Cross-correlation (NCC) loss and Match Patch (MP) loss. NCC loss is constructed based on the normalized Cross-correlation matching algorithm. MP loss is calculated by modeling template matching as a contrastive learning problem. In experiments on the KAIST, VEDAI, and AVIID datasets, this method outperforms state-of-the-art methods in terms of image generation quality and template matching accuracy. Our method incorporates the image matching process into image-to-image (I2I) translation, demonstrating the usability of GANs-based image generation for critical downstream tasks. This research resolves the practical controversy of generating images based on GANs and provides a theoretical reference for image generation for tasks such as multi-source image object detection and data association.

Small object detection in remote sensing images provides significant value for urban monitoring, aerospace reconnaissance, and other fields. However, detection accuracy still faces multiple challenges including limited target information, weak feature representation, and complex backgrounds. This research aims to improve the performance of the YOLO11 model for small object detection in remote sensing imagery by addressing key issues in long-distance spatial dependency modeling, multi-scale feature adaptive fusion, and computational efficiency. We constructed a specialized Remote Sensing Airport-Plane Detection (RS-APD) dataset and used the public VisDrone2019 dataset for generalization verification. Based on the YOLO11 architecture, we proposed the DAE-YOLO model with three innovative modules: Dynamic Spatial Sequence Module (DSSM) for enhanced long-distance spatial dependency capture; Adaptive Multi-scale Feature Enhancement (AMFE) for multi-scale feature adaptive receptive field adjustment; and Efficient Dual-level Attention Mechanism (EDAM) to reduce computational complexity while maintaining feature expression capability. Experimental results demonstrate that compared to the baseline YOLO11, our proposed model improved mAP₅₀ and mAP_50:95 on the RS-APD dataset by 2.1% and 2.5%, respectively, with AP_s increasing by 2.8%. This research provides an efficient and reliable small object detection solution for remote sensing applications.

The preparation of infrared reference images is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared data on-site is difficult and time-consuming. Fortunately, the infrared images can be obtained from the corresponding visible-light images to enrich the infrared data. To this end, this present work proposes an image translation algorithm that converts visible-light images to infrared images. This algorithm, named V2IGAN, is founded on the visual state space attention module and multi-scale feature contrastive learning loss. Firstly, we introduce a visual state space attention module designed to sharpen the generative network’s focus on critical regions within visible-light images. This enhancement not only improves feature extraction but also bolsters the generator’s capacity to accurately model features, ultimately enhancing the quality of generated images. Furthermore, the method incorporates a multi-scale feature contrastive learning loss function, which serves to bolster the robustness of the model and refine the detail of the generated images. Experimental results show that the V2IGAN method outperforms existing typical infrared image generation techniques in both subjective visual assessments and objective metric evaluations. This suggests that the V2IGAN method is adept at enhancing the feature representation in images, refining the details of the generated infrared images, and yielding reliable, high-quality results.

This paper proposes a novel framework for the online fault-tolerant control of hypersonic flight vehicles (HFV). The framework contains two steps. Firstly, based on the Internet of Things (IoT) and digital twin (DT) technology, a hypersonic flight vehicle digital twin (HFVDT) model is constructed. This HFVDT model can represent the real model and the parameter changes in HFV in real-time, and can update itself by the designed updating law through the flight data acquired by IoT. Then, the model changes caused by a fault can be fed back in real-time. Based on the real-time model, a model predictive static programming (MPSP) based controller design method is proposed to solve the online fault-tolerant control problem of HFV. MPSP provides a feasible online solution to optimal control problems with constraints. By converting the nonlinear optimal control problem to a static optimization the problem, the quantity of computation is greatly reduced, and then the optimal can be solved online. By the HFVDT and MPSP framework, the model change can be monitored and then an optimal controller can be constructed in real time. In this case, the fault-tolerant control problem of HFV can solved and the tracking performance is guaranteed. Finally, the novel framework is carried out on a numeral simulation to show its effectiveness.