Abstract
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.
Abstract (translated)
这项研究详细介绍了基于人工智能（AI）的方法，用于对空间相机故障进行语义分割。具体而言，我们解决了由于太阳在相机视场（FoV）周围存在而引起的杂光效应的分割问题。异常图像来源于我们已发布的数据集。我们的方法强调跨多种耀斑纹理的一般化能力，并利用公开数据集（Flare7k++），该数据集中包含各种非空间环境中的耀斑，以缓解真实太空特定数据不足的问题。 采用带有MobileNetV3骨干网络的DeepLabV3模型来执行分割任务。模型设计旨在适应航天器资源受限硬件的需求。最后，基于我们模型与机载导航管道之间提出的接口，开发了自定义指标以评估模型在系统级别上下文中的性能。
URL
https://arxiv.org/abs/2602.20709
