Onboard-Targeted Segmentation of Straylight in Space Camera Sensors

TL;DR

This paper presents an AI-based semantic segmentation method for detecting straylight effects in space camera sensors, utilizing pre-training on diverse flare datasets to improve generalization and deploying a lightweight DeepLabV3 model suitable for spacecraft hardware.

Contribution

It introduces a novel AI approach with pre-training on non-space flare data and a lightweight model for real-time onboard straylight segmentation in space cameras.

Findings

Effective segmentation of straylight effects demonstrated.

Pre-training on Flare7k++ improves model generalization.

Model optimized for resource-constrained spacecraft hardware.

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.