SSOD-YOLO: Mamba-Driven Feature Extraction With Adaptive Feature Enhancement for Small-Object Detection in Space

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Abstract
Small space object detection (SSOD) plays a crucial role in orbital debris monitoring and spacecraft defense, providing critical support for space situational awareness (SSA). To adapt complex background interference and lightweight deployment, a Mamba-driven multi-level feature fusion network is designed based on YOLO architecture. Specifically, an adaptive feature enhancement module (AFEM) is introduced to effectively incorporate multi-level information. To consolidate valid information while suppressing redundant noise, a Mamba-driven dual-branch feature extraction module (DFEM) is developed for spatial-aware and channel-aware selective scanning and merging. Comparative experiments demonstrate that the proposed SSOD-YOLO achieves superior performance on a public small space object dataset, with 95.21% Recall, 97.94% mAP50, and 96.12% F1-score, outperforming both the YOLO series models and the space object detection methods.

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