Abstract
The Chang'e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole-Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed. The model first incorporated a Partial Self-Attention (PSA) mechanism at the end of the Backbone, allowing the model to enhance global perception and reduce missed detections with a low computational cost. Then, a Gather-and-Distribute mechanism (GD) was integrated into the Neck, enabling the model to fully fuse multi-level feature information and capture global information, enhancing the model's ability to detect impact craters of various sizes. The experimental results showed that the YOLOv8-LCNET model performs well in the impact crater detection task, achieving 87.7% Precision, 84.3% Recall, and 92% AP, which were 24.7%, 32.7%, and 37.3% higher than the original YOLOv8 model. The improved YOLOv8 model was then used for automatic crater detection in the CE-6 landing area (246 km × 135 km, with a DOM resolution of 3 m/pixel), resulting in a total of 770,671 craters, ranging from 13 m to 19,882 m in diameter. The analysis of this impact crater catalogue has provided critical support for landing site selection and characterization of the CE-6 mission and lays the foundation for future lunar geological studies.