To address insufficient real-time detection accuracy of standard YOLO models under adverse weather, we propose PFOD-Net, a multi-scale detector based on polarimetric features and an improved YOLOv8n. Compared with traditional intensity imaging, polarimetric imaging extracts optical information from target scenes more effectively; when coupled with an appropriate feature selection mechanism, it significantly enhances detection and recognition performance in complex environments. Experimental results on the Polar LITIS dataset demonstrate that PFOD-Net significantly outperforms YOLOv8 in both accuracy and speed: mAP@0.5 (mean Average Precision) increased from 33.5% to 81.7%—an increase of 48.2 percentage points. Notably, the detection performance of PFOD-Net is substantially reinforced in complex conditions like fog and haze. By innovatively integrating polarimetric information, lightweight architectures, and the fusion of max-pooling and average-pooling, this method provides an effective solution for object detection and recognition in adverse weather.

Small-target detection in remote sensing presents three fundamental challenges: limited pixel representation of targets, multi-angle imaging-induced appearance variance, and complex background interference. This paper introduces a dual-component neural architecture comprising Hierarchical Dynamic Refinement Attention (HiDRA) and Densely Connected Dilated Block (DCDBlock) to address these challenges systematically. The HiDRA mechanism implements a dual-phase feature enhancement process: channel competition through bottleneck compression for discriminative feature selection, followed by spatial-semantic reweighting for foreground–background decoupling. The DCDBlock architecture synergizes multi-scale dilated convolutions with cross-layer dense connections, establishing persistent feature propagation pathways that preserve critical spatial details across network depths. Extensive experiments on AI-TOD, VisDrone, MAR20, and DOTA-v1.0 datasets demonstrate our method’s consistent superiority, achieving average absolute gains of +1.16% (mAP₅₀), +0.93% (mAP₉₅), and +1.83% (F1-score) over prior state-of-the-art approaches across all benchmarks. With 8.1 GFLOPs computational complexity and 2.6 ms inference speed per image, our framework demonstrates practical efficacy for real-time remote sensing applications, achieving superior accuracy–efficiency trade-off compared to existing approaches.

The distribution of diffuse spot energy can be used to sensitively evaluate the aberrations and defects of optical systems. Therefore, the objective and quantitative measurement of diffuse spot parameters is an important means to control the detection quality of space debris detection systems. At present, the existing optical system dispersion measurement method can only judge whether the energy distribution meets the index. However, these methods ca not provide an objective quantitative basis to guide the installation process. To solve this problem, a mathematical simulation model of 3D multi-phase sub-pixel PSF distribution is proposed. According to the relation between the CCD target plane and the theoretical image plane (focal plane, defocus, and deflection), the diffuse spot distribution of the optical system is simulated with different phase combinations. Then, Pearson Correlation Coefficient (PCC) is used to evaluate the matching similarity of the diffuse spot image. The simulation results show that when the PCC is greater than 0.96, the distribution of the two diffuse spots can be identified as matching. This also confirms the accuracy of the proposed PSF model. Then, the focusing deviation of the system being tested can be analyzed according to the phase size of the diffuse spot simulation image. This method can quickly and accurately guide the focal surface installation and testing of the system. Therefore, the purpose of improving the detection accuracy of space debris is achieved. It also provides a quantitative basis for the engineering application of optical detection systems in the future.

Short-wave infrared star sensors have become a key technology for all-time attitude determination within the atmosphere, in which the star identification algorithm plays a fundamental role. However, due to the limited number of detectable stars in infrared images, achieving robust and accurate identification remains challenging. To address this issue, this paper proposes a star identification algorithm based on ordered angular distance verification. The algorithm first extracts radial and adjacency features via full-field-of-view sorting to mitigate the impact of “edge loss”. It then employs a fast initial matching that combines hash table lookup with binary search, substantially reducing the number of candidate navigation stars requiring detailed matching. Subsequently, a local search matching procedure corrects index misalignment caused by false or missing stars, while angular distance invariance verification prevents false matches; the combination of these mechanisms significantly enhances the algorithm’s robustness. In simulations using 5000 star images, the proposed algorithm achieves an identification rate of 99.48%. It maintains a rate above 96% under position noise, magnitude noise, and false stars. The average processing time per star image is 10.57 ms, approximately 39% of that required by the conventional grid algorithm (27.01 ms). The simulation results demonstrate that the proposed algorithm achieves high identification accuracy and maintains strong robustness in complex noise environments.