Fireball streak detection with minimal CPU processing requirements for the Desert Fireball Network data processing pipeline. (2020)

• Record Type: Journal Article
• Authors: Towner, Martin C.; Cupak, Martin; Deshayes, Jean; Howie, Robert M.; Hartig, Ben A. D.; Paxman, Jonathan; Sansom, Eleanor K.; Devillepoix, Hadrien A. R.; Jansen-Sturgeon, Trent; Bland, Philip A.
• Is Part Of: Publications of the Astronomical Society of Australia. Volume 37(2020)
• Publication Date: 2020
• Subjects: Hough transform, -- image processing, -- astronomy; Astronomy -- Periodicals; Southern sky (Astronomy) -- Periodicals; Astrophysics -- Periodicals; Periodicals; 520.5
• DOI: 10.1017/pasa.2019.48
• Languages: English
• ISSNs: 1323-3580

Abstract

Abstract: The detection of fireballs streaks in astronomical imagery can be carried out by a variety of methods. The Desert Fireball Network uses a network of cameras to track and triangulate incoming fireballs to recover meteorites with orbits and to build a fireball orbital dataset. Fireball detection is done on-board camera, but due to the design constraints imposed by remote deployment, the cameras are limited in processing power and time. We describe the processing software used for fireball detection under these constrained circumstances. Two different approaches were compared: (1) A single-layer neural network with 10 hidden units that were trained using manually selected fireballs and (2) a more traditional computational approach based on cascading steps of increasing complexity, whereby computationally simple filters are used to discard uninteresting portions of the images, allowing for more computationally expensive analysis of the remainder. Both approaches allowed a full night's worth of data (over a thousand 36-megapixel images) to be processed each day using a low-power single-board computer. We distinguish between large (likely meteorite-dropping) fireballs and smaller fainter ones (typical 'shooting stars'). Traditional processing and neural network algorithms both performed well on large fireballs within an approximately 30 000-image dataset, with a true positive detection rate of 96% and 100%, respectively, but the neural network was significantly more successful at smaller fireballs, with rates of 67% and 82%, respectively. However, this improved success came at a cost of significantly more false positives for the neural network results, and additionally the neural network does not produce precise fireball coordinates within an image (as it classifies). Simple consideration of the network geometry indicates that overall detection rate for triangulated large fireballs is calculated to be better than 99.7% and 99.9%, by ensuring that there are multiple double-station opportunities to detect any one fireball. As such, both algorithms are considered sufficient for meteor-dropping fireball event detection, with some consideration of the acceptable number of false positives compared to sensitivity.