沙漠火球网络数据处理管道的火球条纹检测和最小的CPU处理要求
Fireball streak detection with minimal CPU processing requirements for the Desert Fireball Network data processing pipeline
摘要：
天文图像中火球条纹的检测可以通过多种方法进行。沙漠火球网络（DFN）使用一个摄像机网络来跟踪和三角测量传入的火球，以恢复陨石的轨道。火球检测是在摄像机上完成的，但由于远程部署的设计限制，摄像机在处理能力和时间上受到限制。我们描述的处理软件用于火球探测在这些约束的情况下。一个级联的方法被实施，从而计算简单的过滤器被用来丢弃不感兴趣的部分的图像，允许更多的计算昂贵的分析的其余部分。这使得一个完整的夜晚的数据价值;超过1000 36万像素的图像处理，每天使用低功耗单板计算机。所选择的算法给出了一个单一的相机成功检测大火球率超过96%，当与人工检查相比，虽然产生了大量的误报。通过确保多个双站有机会检测到一个火球，三角测量的大火球的整体网络检测率估计优于99.8%。
英文摘要：
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 detection large fireball rate of better than 96 percent, when compared to manual inspection, although significant numbers of false positives are generated. The overall network detection rate for triangulated large fireballs is estimated to be better than 99.8 percent, by ensuring that there are multiple double stations chances to detect one fireball.
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