Onboard-Targeted Segmentation of Straylight in Space Camera Sensors
This work addresses a domain-specific problem for space missions by enabling onboard fault detection in cameras, but it is incremental as it adapts existing methods to a new application.
The study tackled the problem of segmenting straylight effects in space camera sensors caused by solar flares, using an AI-based semantic segmentation approach, and achieved deployment-ready performance for resource-constrained spacecraft hardware with custom system-level metrics.
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.