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.

The study of impact craters is crucial in understanding planetary evolution and geological processes, particularly small craters, which are essential for reconstructing the lunar impact history and geological timeline. However, due to the power-law distribution of crater sizes and the complex topography of the lunar surface, detecting small craters on a global scale remains a significant challenge. This study proposes an innovative sample creation method and develops a deep learning framework for small target detection, YOLO-SCNet, aimed at detecting small lunar craters with diameters ranging from 0.2 to 2 kilometers. By combining a high-quality, diversified sample dataset generated using data augmentation techniques with the YOLO-SCNet, specifically designed for small target detection, we successfully addressed key challenges in lunar crater detection. Experimental results show that YOLO-SCNet excels in adapting to complex terrains and varying lighting conditions, achieving 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 YOLO-SCNet framework demonstrates the immense potential of deep learning technologies in planetary geological research. It can be used to construct a global, high-precision lunar crater catalog (≥0.2 km), helping to fill the gap in the global lunar crater database for small craters. Moreover, the framework is highly scalable, with the potential to be extended to other planetary bodies, such as Mars and Mercury, providing significant support for future planetary exploration and mapping tasks.