Abstract Crater cataloging is an important yet time–consuming part of geological mapping. We present an automated Crater Detection Algorithm (CDA) that is competitive with expert–human researchers and hundreds of times faster. The CDA uses multiple neural networks to process digital terrain model and thermal infra–red imagery to identify and locate craters across the surface of Mars. We use additional post-processing filters to refine and remove potential false crater detections, improving our precision and recall by 10% compared to Lee (2019). We now find 80% of known craters above 3km in diameter, and identify 7,000 potentially new craters (13% of the identified craters). The median differences between our catalog and other independent catalogs is 2%–4% in location and diameter, in–line with other inter–catalog comparisons. The CDA has been used to process global terrain maps and infra–red imagery for Mars, and the software and generated global catalog are available at https://doi.org/10.5683/SP2/CFUNII .

中文翻译：

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具有人类水平性能的自动陨石坑检测

摘要 火山口编目是地质填图的一个重要但耗时的部分。我们提出了一种自动陨石坑检测算法 (CDA)，它可以与专家 - 人类研究人员竞争并且速度提高数百倍。CDA 使用多个神经网络来处理数字地形模型和热红外图像，以识别和定位火星表面的陨石坑。我们使用额外的后处理过滤器来改进和去除潜在的错误陨石坑检测，与 Lee（2019）相比，我们的精度和召回率提高了 10%。我们现在找到了 80% 的直径超过 3 公里的已知陨石坑，并确定了 7,000 个潜在的新陨石坑（占已识别陨石坑的 13%）。我们的目录与其他独立目录之间的位置和直径差异中位数为 2%–4%，与其他目录间比较一致。