Crater mapping using neural networks and other automated methods has
increased recently with automated Crater Detection Algorithms (CDAs) applied to
planetary bodies throughout the solar system. A recent publication by Benedix
et al. (2020) showed high performance at small scales compared to similar
automated CDAs but with a net positive diameter bias in many crater candidates.
I compare the publicly available catalogs from Benedix et al. (2020) and Lee &
Hogan (2021) and show that the reported performance is sensitive to the metrics
used to test the catalogs. I show how the more permissive comparison methods
indicate a higher CDA performance by allowing worse candidate craters to match
ground-truth craters. I show that the Benedix et al. (2020) catalog has a
substantial performance loss with increasing latitude and identify an image
projection issue that might cause this loss. Finally, I suggest future
applications of neural networks in generating large scientific datasets be
validated using secondary networks with independent data sources or training
methods.

用神经网络和其他自动化方法进行撞击坑映射的研究近年来有所增加，应用于太阳系各个行星体的自动化撞击坑检测算法（CDAs）。本文比较了 Benedix 等人（2020）和 Lee＆Hogan（2021）的公开可用目录，并展示了使用测试目录的指标对报告性能敏感。作者通过采用更宽松的比较方法指出，允许较差的候选撞击坑与真实撞击坑匹配可以获得更高的 CDA 性能。作者展示了 Benedix 等人（2020）的目录在不断增加的纬度下有较大的性能损失，并确定了可能导致此损失的图像投影问题。最后，作者建议使用独立数据源或训练方法的辅助网络对生成的大型科学数据集中神经网络的应用进行验证。