DETR-Based Query Network for Lunar Crater Exploration

Lunar exploration is progressing rapidly, with craters serving as critical landmarks for precise spacecraft navigation and landing. Although deep learning has enhanced conventional crater identification techniques, persistent limitations include significant computational overhead and pronounced reliance on high-quality data. This study introduces SparseDLE, an efficient real-time crater detector built upon an optimized DETR framework. The method introduces a sparse query selection strategy to progressively refine results from coarse to fine; proposes a morphable grid attention moduleto enhance algorithmic efficiency; and designs a quality match optimizer to improve model accuracy. Experiments demonstrate that the proposed algorithm achieves an F1-score of 84.49%, outperforming existing state-of-the-art methods.