AutomaticCraterDetector

Understanding the distribution and characteristics of impact craters on planetary surfaces is essential for unraveling geological processes and the evolution of celestial bodies. Several machine learning and AI-based approaches have been proposed to detect craters on planetary surface images automatically. However, designing a robust tool for an entire complex planet such as Mars, is still an open problem. Attach to this project, we wrote an article which presents a novel approach using the Faster Region-based Convolutional Neural Network (Faster R-CNN) for such a detection. The proposed method involves the pre-processing, training and crater detection steps, which are especially designed for robustness regarding latitude and complex geomorphological features. Here we propose an open-source and re-usable crater detection algorithm, along with its training dataset and weights, that have been tested on Mars and shows states of art perfomances. Our results also highlight the versatility and potential of our robust model for automating the analysis of craters across different celestial bodies. The automated crater detection tool presented in this article is publicly available as open-source and holds great promise for future scientific research of space exploration missions. please cite Martinez et al, 2025, Robust automatic crater detection at all latitudes on Mars with Deep-learning  , PSS