YOLO-SCNet: A Framework for Enhanced Detection of Small Lunar Craters

The study of impact craters is crucial for understanding planetary evolution and geological processes, particularly small craters, which are key to reconstructing the lunar impact history. Detecting small craters, with diameters ranging from 0.2 to 2 km, remains a challenge due to the power-law distribution of crater sizes and the complex topography of the lunar surface. This work uses high-resolution lunar imagery data from the Chang’E-2 mission, with a 7 m spatial resolution, to develop a deep learning framework for small crater detection, named YOLO-SCNet. The framework combines a high-quality, diversified sample dataset, generated through data augmentation techniques, with YOLO-SCNet, specifically designed for small target detection. Key challenges in lunar crater detection, such as varying lighting conditions and complex terrains, are addressed through the innovative model architecture, which incorporates a small object detection head, dynamic anchor boxes, and multi-scale feature fusion. Experimental results demonstrate that YOLO-SCNet achieves outstanding performance in detecting small craters across different lunar regions, with precision, recall, and F1 scores of 90.2%, 88.7%, and 89.4%, respectively. The framework offers a scalable solution for constructing a global lunar crater catalog (≥0.2 km) and can be extended to other planetary bodies like Mars and Mercury, significantly supporting future planetary exploration and mapping efforts.