Real-time space object tracklet extraction from telescope survey images with machine learning

• Main Authors: Cipollone, R. (Author), De Vittori, A. (Author), Di Lizia, P. (Author), Massari, M. (Author)
• Format: Article
• Language: English
• Published: Tsinghua University 2022
• Subjects: Deep neural networks; Image segmentation; Machine learning; machine learning (ML); Pipeline processing systems; Pipelines; Post-processing; Real images; Real- time; space debris; Space debris; Space objects; Space surveillance; Space surveillance and tracking; space surveillance and tracking (SST); Statistical tests; Telescope image; telescope images; Telescopes; Time-space; tracklet; Tracklets; U-net; U-Net
• Online Access: View Fulltext in Publisher

 In this study, a novel approach based on the U-Net deep neural network for image segmentation is leveraged for real-time extraction of tracklets from optical acquisitions. As in all machine learning (ML) applications, a series of steps is required for a working pipeline: dataset creation, preprocessing, training, testing, and post-processing to refine the trained network output. Online websites usually lack ready-to-use datasets; thus, an in-house application artificially generates 360 labeled images. Particularly, this software tool produces synthetic night-sky shots of transiting objects over a specified location and the corresponding labels: dual-tone pictures with black backgrounds and white tracklets. Second, both images and labels are downscaled in resolution and normalized to accelerate the training phase. To assess the network performance, a set of both synthetic and real images was inputted. After the preprocessing phase, real images were fine-tuned for vignette reduction and background brightness uniformity. Additionally, they are down-converted to eight bits. Once the network outputs labels, post-processing identifies the centroid right ascension and declination of the object. The average processing time per real image is less than 1.2 s; bright tracklets are easily detected with a mean centroid angular error of 0.25 deg in 75% of test cases with a 2 deg field-of-view telescope. These results prove that an ML-based method can be considered a valid choice when dealing with trail reconstruction, leading to acceptable accuracy for a fast image processing pipeline. [Figure not available: see fulltext.] © 2022, The Author(s).