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 se...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.展开更多
文摘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.
