Article Highlights (1) A lightweight cross-scale feature fusion module is introduced to improve the feature fusion capability of the model. (2) The content-aware reassembly of feature module is introduced to enhance the quality of feature reconstruction. (3) Experiments are conducted with real datasets to verify the advantages of the improved method.

Optical sensor systems are essential for space target detection. However, previous studies have prioritized detection accuracy over model efficiency, limiting their deployment on resource-constrained sensors. To address this issue, we propose the lightweight space target real-time detection transformer (STar-DETR), which achieves a balance between model efficiency and detection accuracy. First, the improved MobileNetv4 (IMNv4) backbone network is developed to significantly reduce the model’s parameters and computational complexity. Second, group shuffle convolution (GSConv) is incorporated into the efficient hybrid encoder, which reduces convolution parameters while facilitating information exchange between channels. Subsequently, the dynamic depthwise shuffle transformer (DDST) feature fusion module is introduced to emphasize the trajectory formed by space target exposure. Finally, the minimum points distance scylla intersection over union (MPDSIoU) loss function is developed to enhance regression accuracy and expedite model convergence. A space target dataset is constructed, integrating offline and online data augmentation techniques to improve robustness under diverse sensing conditions. The proposed STar-DETR model achieves an $A{P}_{0.5:0.95}$ of 89.9%, successfully detecting dim and discontinuous streak space targets. Its parameter count and computational complexity are reduced by 64.8% and 41.8%, respectively, highlighting its lightweight design and providing a valuable reference for space target detection in resource-constrained optical sensors.

The design of an Active Reflective Surface Control System (ARCS) is a complex engineering task involving multidimensional and multi-criteria constraints. This paper proposes a novel methodological approach for ARCS design and optimization by integrating Axiomatic Design (AD) and Multi-Criteria Decision Making (MCDM) techniques. Initially, a structured design plan is formulated within the axiomatic design framework. Subsequently, four MCDM methods—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Entropy Weight Method (EWM), Multi-Criteria Optimization and Compromise Solution (VIKOR), and the integrated TOPSIS–Grey Relational Analysis (GRA) approach—are used to evaluate and compare the alternative solutions. Additionally, fuzzy information axioms are used to calculate the total information content for each alternative to identify the optimal design. A case study is conducted, selecting the optimal actuator for a 5 m diameter scaled model of the Five-hundred-meter Aperture Spherical radio Telescope (FAST), followed by digital control experiments on the chosen actuator. Based on the optimal design scheme, an ARCS prototype is constructed, which accelerates project completion and substantially reduces trial-and-error costs.