| Summary: | Abstract Impact craters are the most common feature on the Moon’s surface. Crater size–frequency distributions provide critical insight into the timing of geological events, surface erosion rates, and impact fluxes. The impact crater size–frequency follows a power law (meter‐sized craters are a few orders of magnitude more numerous than kilometric ones), making it tedious to manually measure all the craters within an area to the smallest sizes. We can bridge this gap by using a machine learning algorithm. We adapted a Crater Detection Algorithm to work on the highest resolution lunar image data set (Lunar Reconnaissance Orbiter‐Narrow‐Angle Camera [NAC] images). We describe the retraining and application of the detection model to preprocessed NAC images and discussed the accuracy of the resulting crater detections. We evaluated the model by assessing the results across six NAC images, each covering a different lunar area at differing lighting conditions. We present the model’s average true positive rate for small impact craters (down to 20 m in diameter) is 93%. The model does display a 15% overestimation in calculated crater diameters. The presented crater detection model shows acceptable performance on NAC images with incidence angles ranging between ∼50° and ∼70° and can be applied to many lunar sites independent to morphology.
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