The recovery of freshly fallen meteorites from tracked and triangulated meteors is critical to determining their source asteroid families. However, locating meteorite fragments in strewn fields remains a challenge with very few meteorites being recovered from the meteors triangulated in past and ongoing meteor camera networks. We examined if locating meteorites can be automated using machine learning and an autonomous drone. Drones can be programmed to fly a grid search pattern and take systematic pictures of the ground over a large survey area. Those images can be analyzed using a machine learning classifier to identify meteorites in the field among many other features. Here, we describe a proof-of-concept meteorite classifier that deploys off-line a combination of different convolution neural networks to recognize meteorites from images taken by drones in the field. The system was implemented in a conceptual drone setup and tested in the suspected strewn field of a recent meteorite fall near Walker Lake, Nevada.
翻译:从履带和三角流星中找回新落的陨石对于确定其来源小行星群至关重要,然而,将陨石碎片定位在碎石场仍是一项挑战,从过去和现在的流星摄影机网络中三角的流星体中找到的陨石极少。我们检查了能否利用机器学习和自主无人机自动找到流星体。可以对无人机进行编程,以在大面积的勘测区进行网格搜索模式飞行和对地面进行系统摄影。这些图像可以用机器学习分类器进行分析,以辨别实地的陨石。这里,我们描述了一个验证概念的陨石分类器,在离线上安装了不同的脉冲神经网络组合,以识别来自无人机在野外拍摄的图像中的流星体。这个系统是在一个概念性无人机中安装的,并在内华达州沃克湖附近最近发生的陨石坠落的可疑的浮流星场进行测试。