An Approach to Lunar Regolith Particle Detection and Classification using Deep Learning
Abstract
Lunar regolith, unconsolidated rock on the lunar surface, is made up of various particles. Understanding the quantities and locations of these particles on the lunar surface is of particular interest to planetary scientists for mission planning and science objectives. There is a limited supply of lunar regolith samples available on Earth for planetary scientists to characterize. Lunar rover missions over the next decade are expected to provide high-resolution images of the lunar surface. Deep learning can be leveraged to analyze lunar regolith from image data. An object detection model using transfer learning was developed to identify and classify particles of in-situ lunar regolith. A custom dataset using micro-images of the lunar surface from the Apollo missions was labelled and processed. Pre-trained Faster R-CNN and Single Shot Detector with ResNet backbone architectures were tested. The results were promising for the application of deep learning using transfer learning on lunar regolith imagery.