Abstract: Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions, yet deep learning still struggles with accurately detecting small craters, especially when relying on incomplete catalogs. In this work, we integrate Digital Elevation Model (DEM) data to construct a high-quality dataset enriched with slope information, enabling a detailed analysis of crater features and effectively improving detection performance in complex terrains and low-contrast areas. Based on this foundation, we propose a novel two-stage detection network, MSFNet, which leverages multi-scale adaptive feature fusion and multi-size ROI pooling to enhance the recognition of craters across various scales. Experimental results demonstrate that MSFNet achieves an F1 score of 74.8% on Test Region1 and a recall rate of 87% for craters with diameters larger than 2 km. Moreover, it shows exceptional performance in detecting sub-kilometer craters by successfully identifying a large number of high-confidence, previously unlabeled targets with a low false detection rate confirmed through manual review. This approach offers an efficient and reliable deep learning solution for lunar impact crater detection.