发表机构
CNES; IRT Saint-Exupéry（法国国家空间研究中心; 圣埃克苏佩里信息技术研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究评估图像退化（SNR、MTF、GSD）对机载目标检测的影响，发现退化影响因机制而异，GSD影响最一致，严重模糊与噪声组合损失最大，为机载系统设计提供权衡依据。
AI 中文摘要
机载人工智能正引起太空应用的兴趣，如船只、野火和云层检测，在这些应用中，实时处理可以提高任务响应能力并减少下行链路需求。然而，机载模型可能处理原始或最小处理的图像，而非经过修复的地面产品。本研究通过改变信噪比（SNR）、奈奎斯特频率下的调制传递函数（MTF）和地面采样距离（GSD），评估图像退化如何影响目标检测。对极高分辨率的Maxar图像施加受控退化，并在由此产生的操作点上评估三种轻量级检测器：YOLOv5s、YOLOX-S和NanoDet。结果表明，图像质量的影响取决于退化机制，且增加退化并不必然导致船只检测性能成比例下降。GSD产生最一致的性能变化，而MTF和SNR的影响更多依赖于模型和分辨率。模糊和噪声的严重组合导致最大损失。这些结果提供了任务级信息，可支持未来机载系统的传感器、处理及人工智能权衡决策。
英文摘要
Onboard AI is gaining interest for space applications such as vessel, wildfire, and cloud detection, where real-time processing can improve mission reactivity and reduce downlink needs. However, onboard models may operate on raw or minimally processed imagery rather than on restored ground products. This study evaluates how image degradation affects object detection by varying Signal-to-Noise Ratio (SNR), Modulation Transfer Function (MTF) at Nyquist, and Ground Sampling Distance (GSD). Controlled degradations are applied to Very High Resolution Maxar imagery, and three lightweight detectors, YOLOv5s, YOLOX-S, and NanoDet, are evaluated on the resulting operating points. The results show that the impact of image quality depends on the degradation mechanism, and that increasing degradation does not necessarily lead to a proportional decrease in vessel detection performance. GSD produces the most consistent performance shift, while MTF and SNR effects depend more on the model and resolution. Severe combinations of blur and noise produce the largest losses. These results provide task-level information that can support sensor, processing, and AI trade-offs for future onboard systems.
CommentsAccepted at OBPDC 2026
