The capacity to detect craters in complex scenes is of paramount importance for the fields of deep space exploration and planetary science. In order to address the accuracy bottleneck in detecting degraded craters within rille regions and at crater margins, this paper proposes the HCF-YOLO crater detection network, which synergistically integrates discrete wavelet transform and cross-attention. The wavelet-domain preserved downsampling module is designed to mitigate the decay of structural features in degraded crater edges. This enhancement of distinguishability from the background is accompanied by a reduction in false negative rates. Secondly, a guided cross-attention fusion module is introduced. The generation of weights from shallow-level crater structural details is achieved in order to directionally constrain cross-scale fusion with high-level semantics. The suppression of rille texture noise interference is of particular significance in this context, as it prevents clear, large craters′ semantic features from masking structural features. This, in turn, serves to reduce false positives caused by background noise and feature confusion. The two modules have been shown to enhance detection robustness in complex scenes in a synergistic manner. Finally, systematic comparisons and ablation experiments are conducted on the MDCD dataset. In comparison with state-of-the-art methods, true positive (TP) detections increase by 133, recall improves by 5%, and precision rises by 2%.