基于 YOLO 的月表撞击坑检测实验方案设计

The automatic extraction of impact craters on the Lunar surface provides a reliable reference for the automatic location of interplanetary probes for landing, and can directly serve the important needs of the military and civil aerospace fields. However, the variable scale and uneven distribution of Lunar craters make it challenging to develop an effective automatic impact crater extraction method. In this paper, a Lunar surface impact crater detection experiment scheme based on YOLO is designed to meet the practical training needs of aerospace information majors. First, the lunar crater training database is built, then the deep learning model based on YOLO series network is trained, and the effectiveness of impact crater detection is tested. Finally, the detection accuracy of the trained network model on the test set can reach 97.7%. Through the design of the experimental scheme, students can deeply understand the ideas and methods of research in the aerospace field in practice, and improve their interest and practical ability in scientific research and exploration.