Ground-based optical equipment for detecting geostationary orbit space targets typically involves long-exposure imaging, facing challenges such as small and blurred target images, complex backgrounds, and star streaks obstructing the view. To address these issues, this study proposes a GSTD-DETR model based on Real-Time Detection Transformer (RT-DETR), which aims to balance model efficiency and detection accuracy. First, we introduce a Dynamic Cross-Stage Partial (DynCSP) backbone network for feature extraction and fusion, which enhances the network’s representational capability by reducing convolutional parameters and improving information exchange between channels. This effectively reduces the model’s parameter count and computational complexity. Second, we propose a ResFine model with a feature pyramid designed for small target detection, enhancing its ability to perceive small targets. Additionally, we improve the detection head and incorporate a Dynamic Multi-Channel Attention mechanism, which strengthens the focus on critical regions. Finally, we designed an Area-Weighted NWD loss function to improve detection accuracy. The experimental results show that compared to RT-DETR-r18, the GSTD-DETR model reduces the parameter count by 29.74% on the SpotGEO dataset. Its AP₅₀ and AP_50:95 improve by 1.3% and 4.9%, reaching 88.6% and 49.9%, respectively. The GSTD-DETR model demonstrates superior performance in the detection accuracy of faint and small space targets.

Laser active imaging systems can remedy the shortcomings of visible light imaging systems in difficult imaging circumstances, thereby attaining clear images. However, laser images exhibit significant modal discrepancy in contrast to the visible image, impeding human perception and computer processing. Consequently, it is necessary to translate laser images to visible images across modalities. Existing cross-modal image translation algorithms are plagued with issues, including difficult training and color bleeding. In recent studies, diffusion models have demonstrated superior image generation and translation abilities and been shown to be capable of generating high-quality images. To achieve more accurate laser-visible image translation, we designed an improved diffusion model, called DCLTV, which limits the randomness of diffusion models by means of dual-condition control. We incorporated the Brownian bridge strategy to serve as the first condition control and employed interpolation-based conditional injection to function as the second condition control. We also established a dataset comprising 665 pairs of laser-visible images to compensate for the data deficiency in the field of laser-visible image translation. Compared to five representative baseline models, namely Pix2pix, BigColor, CT2, ColorFormer, and DDColor, the proposed DCLTV achieved the best performance in terms of both qualitative and quantitative comparisons, realizing at least a 15.89% reduction in FID and at least a 22.02% reduction in LPIPS. We further validated the effectiveness of the dual conditions in DCLTV through ablation experiments, achieving the best results with an FID of 154.74 and an LPIPS of 0.379.

To further study the electro-optical modulation characteristics of LiNbO₃ crystals and analyze their modulation performance, a method for studying the modulation characteristics of LiNbO₃ crystals, based on the three-dimensional ray tracing method, is introduced. With the help of the refractive index ellipsoidal theory, the optical properties of LiNbO₃ crystals under the influence of the Pockels effect are systematically investigated. The research results show that the optical properties of LiNbO₃ crystals under the action of an external electric field can be divided into two cases: the crystal optical axis is parallel to the clear light direction, and the crystal optical axis is perpendicular to the clear light direction. Subsequently, starting from Maxwell’s equation and the matter equation, the analytical expressions of optical parameters such as refractive index, wave vector, light vector, optical path, and phase delay in electro-optical crystals are derived. Finally, the propagation law of LiNbO₃ crystals when the light is incident in any direction, i.e., when the optical axis of the crystal is parallel to the clear direction and perpendicular to the clear direction, and the light intensity and field of view of the LiNbO₃ crystal for electro-optical modulation are discussed.

LiDAR offers a wide range of uses in autonomous driving, remote sensing, urban planning, and other areas. The laser 3D point cloud acquired by LiDAR typically encounters issues during registration, including laser speckle noise, Gaussian noise, data loss, and data disorder. This work suggests a novel Student’s t-distribution point cloud registration algorithm based on the local features of point clouds to address these issues. The approach uses Student’s t-distribution mixture model (SMM) to generate the probability distribution of point cloud registration, which can accurately describe the data distribution, in order to tackle the problem of the missing laser 3D point cloud data and data disorder. Owing to the disparity in the point cloud registration task, a full-rank covariance matrix is built based on the local features of the point cloud during the objective function design process. The combined penalty of point-to-point and point-to-plane distance is then added to the objective function adaptively. Simultaneously, by analyzing the imaging characteristics of LiDAR, according to the influence of the laser waveform and detector on the LiDAR imaging, the composite weight coefficient is added to improve the pertinence of the algorithm. Based on the public dataset and the laser 3D point cloud dataset acquired in the laboratory, the experimental findings demonstrate that the proposed algorithm has high practicability and dependability and outperforms the five comparison algorithms in terms of accuracy and robustness.

A method for enhancing the resolution of 3D imaging reconstruction by employing the polarization modulation of electro-optical crystals is proposed. This technique utilizes two polarizers oriented perpendicular to each other along with an electro-optical modulation crystal to achieve high repetition frequency and narrow pulse width gating. By varying the modulation time series of the electro-optical crystal, three-dimensional gray images of the laser at different distances are acquired, and the three-dimensional information of the target is reconstructed using the range energy recovery algorithm. This 3D imaging system can be implemented with large area detectors, independent of the an Intensified Charge-Coupled Device (ICCD) manufacturing process, resulting in improved lateral resolution. Experimental results demonstrate that when imaging a target at the distance of 20 m, the lateral resolution within the region of interest is 2560 × 2160, with a root mean square error of 3.2 cm.

In this paper, we construct a laser 3D imaging simulation model based on the 3D imaging principle of electro–optical crystal modulation. Unlike the traditional 3D imaging simulation method, this paper focuses on the laser scattering characteristics of the target scene. To accurately analyze and simulate the scattering characteristic model of the target under laser irradiation, we propose a BRDF (Bidirectional Reflectance Distribution Function) model fitting algorithm based on the hybrid BBO–Firefly model, which can accurately simulate the laser scattering distribution of the target at different angles. Finally, according to the fitted scattering characteristic model, we inverted the target imaging gray map. We used the laser 3D imaging restoration principle to reconstruct the 3D point cloud of the target to realize the laser 3D imaging of the target.