A Deep Learning Approach for Lunar Impact Crater Detection Based on YOLO v7 and CBAM Attention Mechanism
Jionghao Zhu, Jiarui Liang, Xiaolin Tian, Pengcheng Yan
Abstract
Impact craters are the most common geomorphic unit on the lunar surface, and there are a large number of impact craters of different sizes and morphologies on the lunar surface. Lunar impact craters are a key basis for lunar geological studies, and their study allows for the exploration of the Moon and other planets. Therefore, this paper builds a convolutional neural network YOLO V7_CBAM based on YOLO V7 and the attention mechanism for identifying lunar impact craters. Based on the full-moon CCD images and DEM images provided by NASA, a comparison test was conducted and the precision of YOLO V7_CBAM based on both images was higher than that of YOLO V7, from 72.31% to 74.65% based on CCD images; and from 70.60% to 71.70% based on DEM images.