The making of infrared templates is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared images from fields is difficult, of high cost, and time‐consuming. In order to address this problem, an infrared image generation method, infrared generative adversarial networks (I‐GANs), based on conditional generative adversarial networks (CGAN) architecture is proposed. In I‐GANs, visible images instead of random noise are used as the inputs, and the D‐LinkNet network is also utilized to build the generative model, enabling improved learning of rich image textures and identification of dependencies between images. Moreover, the PatchGAN architecture is employed to build a discriminant model to process the high‐frequency components of the images effectively and reduce the amount of calculation required. In addition, batch normalization is used to optimize the training process, and thereby, the instability and mode collapse of the generated adversarial network training can be alleviated. Finally, experimental verification is conducted on the produced infrared/visible light dataset (IVFG). The experimental results reveal that high‐quality and reliable infrared data are generated by the proposed I‐GANs.

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.

To realize the error parameter estimation of strap-down inertial navigation system (SINS) and improve the navigation accuracy for aircraft, a hybrid improved restricted Boltzmann machine BP neural network (IRBM-BPNN) approach, which combines restricted Boltzmann machine (RBM) and BP neural network (BPNN), is proposed to forecast the inertial measurement unit (IMU) instrument errors and initial alignment errors of SINS. Firstly, the error generation mechanism of SINS is analyzed, and initial alignment error model and IMU instrument error model are established. Secondly, an unsupervised RBM method is introduced to initialize BPNN to improve the forecast performance of the neural network. The RBM-BPNN model is constructed through the information fusion of SINS/GPS/CNS integrated navigation system by using the sum of position deviation, the sum of velocity deviation and the sum of attitude deviation as the inputs and by using the error parameters of SINS as the outputs. The RBM-BPNN structure is improved to enhance its forecast accuracy, and the pulse signal is increased as the input of the neural network. Finally, we conduct simulation experiments to forecast and compensate the error parameters of the proposed IRBM-BPNN method. Simulation results show that the artificial neural network method is feasible and effective in forecasting SINS error parameters, and the forecast accuracy of SINS error parameters can be effectively improved by combining RBM and BPNN methods and improving the neural network structure. The proposed IRBM-BPNN method has the optimal forecast accuracy of SINS error parameters and navigation accuracy of aircraft compared with the radial basis function neural network method and BPNN method.