用AI识别太空相机因太阳引起的杂散光，提升星载系统可靠性。
Onboard-Targeted Segmentation of Straylight in Space Camera Sensors
- 基于DeepLabV3+MobileNetV3模型，利用公开数据预训练增强泛化能力。
- 在真实太空图像上实现杂散光区域精确分割，支持星载部署。
- 设计系统级评估接口，适配航天器资源受限场景下的故障检测。
本研究提出一种基于人工智能的语义分割方法，用于识别空间相机因太阳进入视场（FoV）引发的杂散光故障。异常图像来自我们发布的数据集。为克服真实太空数据稀缺问题，模型在包含多种非太空场景耀斑的公开数据集Flare7k++上进行预训练，以增强对不同耀斑纹理的泛化能力。采用DeepLabV3架构搭配MobileNetV3骨干网络完成分割任务，兼顾精度与轻量化，适合部署于资源受限的航天器硬件。最后，基于模型与星载导航流程间的接口设计，构建定制化评估指标，从系统层面衡量其性能。
原文摘要 · Abstract (English)
This study details an artificial intelligence (AI)-based methodology for the semantic segmentation of space camera faults. Specifically, we address the segmentation of straylight effects induced by solar presence around the camera's Field of View (FoV). Anomalous images are sourced from our published dataset. Our approach emphasizes generalization across diverse flare textures, leveraging pre-training on a public dataset (Flare7k++) including flares in various non-space contexts to mitigate the scarcity of realistic space-specific data. A DeepLabV3 model with MobileNetV3 backbone performs the segmentation task. The model design targets deployment in spacecraft resource-constrained hardware. Finally, based on a proposed interface between our model and the onboard navigation pipeline, we develop custom metrics to assess the model's performance in the system-level context.
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