Shuai Han

Visual perception for autonomous carrier landing requires high stability and environmental adaptability. Under complex maritime conditions, such as nighttime, sea fog, and low visibility, visible images are easily affected by illumination changes and atmospheric scattering, resulting in reduced target contrast and weakened scene structures. Infrared images can provide relatively stable target information through thermal radiation differences between targets and backgrounds, but offer limited texture and fine-grained structural details. To address the difficulty of simultaneously preserving target saliency and scene structural integrity with a single modality, we propose CLSC-Fusion, an infrared–visible image fusion method for enhanced visual carrier-landing perception. Based on FusionGAN, CLSC-Fusion introduces an infrared gradient loss and a visible intensity loss to enhance the preservation of infrared salient information and visible details. An eight-neighborhood Laplacian operator with diagonal neighborhoods is further introduced into the gradient constraint to strengthen responses to multi-directional edges and texture variations. Meanwhile, the generator architecture is improved to enhance the representation of fusion features. Experiments on the TNO dataset show that CLSC-Fusion achieves competitive results in Spatial Frequency, Average Gradient, edge preservation, and PSNR, while maintaining high levels of Standard Deviation and SSIM. Ablation studies further verify the effectiveness of the proposed improvements. Case studies in visual carrier-landing scenarios demonstrate that CLSC-Fusion effectively integrates infrared target responses with structural information from flight decks, islands, and sea–sky backgrounds, providing more complete visual information for carrier-based aircraft perception under complex environments.

In various engineering fields, the fusion of infrared and visible images has important applications. However, in the current process of fusing infrared and visible images, there are problems with unclear texture details in the fused images and unbalanced displays of infrared targets and texture details, resulting in information loss. In this article, we propose an improved generative adversarial network (GAN) fusion model for fusing infrared and visible images. In the generator and discriminator network structure, we introduce densely connected blocks to connect the features between layers, improve network efficiency, enhance the network’s ability to extract source image information, and construct a content loss function using four losses, including an infrared gradient, visible intensity, infrared intensity, and a visible gradient, to maintain a balance between infrared radiation information and visible texture details, enabling the fused image to achieve ideal results. The effectiveness of the fusion method is demonstrated through ablation experiments on the TNO dataset, and compared with four traditional fusion methods and three deep learning fusion methods. The experimental results show that our method achieves five out of ten optimal evaluation indicators, with a significant improvement compared to other methods.

Aiming at the problems of land planning, natural disasters and campus building safety in China in the past two years, the aerial image of unmanned aerial vehicle is combined with outdoor 3D reconstruction technology. Based on hierarchical key frame selection algorithm and linear segmentation feature point detection extraction algorithm, images were selected and feature points were extracted and matched, and a set of efficient, convenient and real 3D reconstruction platform for outdoor large-scale scenes was established, which can play an important role in bridge inspection, geological inspection and building planning.