Deep Learning Techniques for Detecting Lunar Craters and Surface Features

Lunar surface analysis plays a pivotal role in unraveling the Moon's geological history and ensuring the safety of future lunar missions. Accurate detection and classification of features such as craters and boulders are essential for understanding impact processes and assessing potential hazards for spacecraft landings. In this project, we introduce a deep learning-based approach for automated lunar surface analysis, focusing on the detection and classification of craters and boulders using segmentation techniques. Moving beyond the YOLO based models, our methodology employs advanced models such as U-Net and Mask R-CNN for precise segmentation, as well as Generative Adversarial Networks (GANs) for data augmentation. The dataset is sourced from highresolution lunar images, including data from NASA's Lunar Reconnaissance Orbiter mission, with carefully annotated labels for both craters and boulders.Key stages of the process include image preprocessing using Python-based tools such as Canny Edge Detection and noise reduction techniques. The segmentation models then extract features, followed by the application of morphological operations for post-processing to refine object boundaries. This comprehensive approach ensures high precision in object detection, with clear labeling and bounding box generation for improved interpretability. Model performance is evaluated rigorously using metrics such as Intersection over Union (IoU),Dice Coefficient, Precision, and Recall. A webbased interface, developed using Flask, enables real-time analysis, allowing users to upload lunar images for immediate crater and boulder detection. This framework enhances the efficiency of lunar surface feature detection and segmentation, with broader implications for planetary exploration and mission planning.