Onboard-Targeted Segmentation of Straylight in Space Camera Sensors

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.

Figures (7)

• Figure 1: Binary segmentation of all dataset fault classes with DeepLabV3. Training data is described in Table \ref{['tab:dataset']}. Training run for 500 epochs with Binary Cross-entropy loss and Adam optimizer with $0.0001$ initial learning rate.
• Figure 2: Sample images synthetically generated and relative segmentation mask from Flare7k++ dai2023flare7k++
• Figure 3: Binary segmentation of straylight. Pre-trained model inferenced on custom test dataset
• Figure 4: Binary segmentation of straylight. Fine-tuned model inferenced on custom dataset. Training takes 500 epochs, employs Binary Cross-entropy loss and Adam optimizer with $0.0001$ initial learning rate.
• Figure 5: Onboard camera sensor data processing scheme