Deep Learning Techniques for Crater Detection on Lunar Surface Images from Chandrayaan-2 Satellite

Abstract

Lunar exploration is pivotal in establishing a human presence on the Moon, and lunar crater detection plays a major role in this pursuit. The study is divided into two key phases: the creation of a specialized annotated dataset sourced from the Optical High-Resolution Camera on the Chandrayaan-2 satellite, and the evaluation of model performance using this dataset. Employing models such as FasterRCNN, YoloV5, and YoloV1, the investigation reveals the YoloV5 model’s superiority, achieving a precision of 92% and a recall of 83% for lunar crater detection. This finding constitutes a significant contribution to lunar exploration research.