mlsanityData to 7 Oct 2026

Onboard-Targeted Segmentation of Straylight in Space Camera Sensors

arXiv 2602.20709 ↗First posted 24 Feb 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 47 references that are in the map. First posted on arXiv 24 Feb 2026.

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25 of 47 references.

References · 25

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13 more
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  13. 2025Addressing Camera Sensors Faults in Vision-Based Navigation: Simulation and Dataset Developmentcited by 1

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