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SSOD-YOLO: Mamba-Driven Feature Extraction With Adaptive Feature Enhancement for Small-Object Detection in Space
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DOI：10.1109/MAES.2025.3641878.png)
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Abstract
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Small space object detection (SSOD) plays a crucial role in orbital debris monitoring and spacecraft defense, providing critical support for space situational awareness. To adapt complex background interference and lightweight deployment, a Mamba-driven multilevel feature fusion network is designed based on you only look once (YOLO) architecture. Specifically, an adaptive feature enhancement module is introduced to effectively incorporate multilevel information. To consolidate valid information while suppressing redundant noise, a Mamba-driven dual-branch feature extraction module 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.
Keywords:
Small space object detection
multi-level
adaptive strategy
dual-branch extraction
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