Fireball streak detection with minimal CPU processing requirements for the Desert Fireball Network data processing pipeline
Martin C. Towner, Martin Cupak, Robert M. Howie, Ben Hartig, Jonathan, Paxman, Eleanor K. Sansom, Hadrien A. R. Devillepoix, Trent Jansen-Sturgeon,, Philip A. Bland

TL;DR
This paper presents a low-power, efficient fireball streak detection software for the Desert Fireball Network, achieving high detection rates with minimal CPU processing on remote cameras.
Contribution
The authors developed a cascading detection algorithm optimized for low-power hardware, enabling effective fireball detection in remote astronomical imaging.
Findings
Over 96% detection rate for large fireballs compared to manual inspection
Processed over 1000 high-resolution images per night on a single low-power computer
Network detection rate for triangulated fireballs exceeds 99.8%
Abstract
The detection of fireballs streaks in astronomical imagery can be carried out by a variety of methods. The Desert Fireball Network--DFN--uses a network of cameras to track and triangulate incoming fireballs to recover meteorites with orbits. Fireball detection is done on-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. A cascading approach was implemented, whereby computationally simple filters are used to discard uninteresting portions of the images, allowing for more computationally expensive analysis of the remainder. This allows a full night's worth of data; over 1000 36 megapixel images to be processed each day using a low power single board computer. The algorithms chosen give a single camera successful…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
