| Citation: | TENG Jia-wei, TIAN Lei, JIANG Shan, SUN Hai-jiang, WANG Rui, YU Zheng-lei. Intelligent recognition and 3D reconstruction method for satellite key components[J]. Chinese Optics. doi: 10.37188/CO.2025-0091
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To achieve the recognition and 3D reconstruction of space target components in complex, low-texture environments for space situational awareness tasks, this paper proposes an end-to-end intelligent perception framework for space targets based on deep learning. This framework enables intelligent recognition and high-precision 3D reconstruction of key space target components. The current space target sensing task is facing great challenges, the surface of on-orbit targets are mostly low-texture metal materials, traditional feature matching methods are ineffective, and the geometrical structure of the components is complex and there is occlusion, so it is necessary to take into account the global semantics and local accuracy. First, based on the lightweight YOLOv11s network, an attention mechanism is introduced to focus features, achieving precise localization and recognition of space targets and their key components while ensuring real-time performance. This facilitates the extraction of target regions for accurate 3D reconstruction. Subsequently, a novel 3D reconstruction algorithm named Sat-TransMVSNet, specifically designed for low-texture space targets, is proposed. This algorithm employs a multi-scale feature enhancement network for feature extraction and utilizes a novel cost volume regularization method to strengthen geometric constraints at space target edges. It incorporates a background-suppression and foreground-enhancement module, combined with a dynamic depth sampling strategy, to accurately reconstruct space targets. Finally, the overall framework is tested using a self-built multi-angle space target dataset comprising various types. Experimental results indicate that the component recognition algorithm achieves an mAP50 of 0.95, and the comprehensive 3D reconstruction error is
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