Hardware-aware, deep-learning approaches for image denoising and star detection for star tracker sensor

• Published in: International Conference on Control, Decision and Information Technologies (Online) pp. 1127 - 1134
• Main Authors: Guesmi, Besma, Moloney, David
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2024
• Subjects: Accuracy; attitude determination; autoencoder; centroiding; Computer architecture; Deep learning; denoising; detection; Detectors; image preprocessing; low-power; Noise; Noise reduction; Position measurement; Space vehicles; star tracker; Stars; YOLO; YOLOv5
• ISSN: 2576-3555
• DOI: 10.1109/CoDIT62066.2024.10708467

In recent years, Deep Neural Networks (DNNs) approaches have outperformed traditional techniques for several computer vision problems. This has been made possible by the increase of computational resources represented by Graphical Processing Units (GPU) that allow training using large datasets and the availability of deep learning accelerators for inference. On the other hand, the attitude determination accuracy requirements for spacecraft are increasing. The most accurate attitude determination sensor for spacecraft is the so-called star sensor or star tracker. With the increase in low-cost satellite platforms such as CubeSats, research into the improvement of star sensor accuracy for low-power and low-cost sensor architectures remains a relevant subject. In this context, we examine several methods for noise reduction and star detection for improving centroiding performance. More specifically, an efficient and robust denoising method for star images using an Auto-Encoder (AE) is proposed. This method enhances the image quality for systems sensitive to noise. Furthermore, an accurate and lightweight algorithm based on an existing YOLO (You Only Look Once) architecture is proposed to detect the location of stars in the image. In this work, the YOLO bounding boxes are used to describe the space region around the stars. Subsequently, the star centroid within the bounding box is computed using the COG (Center Of Gravity) method. This method removes the need for centroiding algorithms sliding over the entire image area. An extensive comparison of the proposed denoising technique with other traditional filters confirms that the proposed method resists all noise models and reconstructs well the corrupted images. Experiments show that the proposed YOLO-based star detector achieves high accuracy with a lightweight architecture without any extra latency.