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.

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

De Vittori A.;Cipollone R.;Di Lizia P.;Massari M.
2022

Abstract

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.
machine learning (ML)
space debris
space surveillance and tracking (SST)
telescope images
tracklet
U-Net
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1211057
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