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FPW-YOLO11n: A Lightweight Frequency-Perception Framework for Lunar Impact Crater Detection
Jiarui Liang1, Pengcheng Yan1, Qi Wen2
1Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, N°S 100-460, Taipa, Macau, China.
Sensors (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces FPW-YOLO11n, an advanced method for automated lunar crater detection from digital elevation models (DEMs). The new model enhances accuracy and efficiency, outperforming previous methods for geological analysis and landing-site selection.
Area of Science:
- Planetary Science: Lunar surface analysis and remote sensing.
- Computer Vision: Object detection and deep learning applications in geospatial data.
Background:
- Automated detection of lunar impact craters from digital elevation model (DEM) data is crucial for geological studies and mission planning.
- Existing methods face challenges due to variations in crater scale, degraded rims, ambiguous boundaries, and complex topography.
- Large-scale lunar remote sensing demands models balancing accuracy, complexity, and inference efficiency.
Purpose of the Study:
- To propose FPW-YOLO11n, a novel frequency-perception crater detection method based on YOLO11n, to address the limitations in automated lunar crater detection.
- To enhance the representation of crater features and improve detection accuracy and efficiency for lunar DEM data.
Main Methods:
- Introduced a Frequency-Directional Attention Module (FDA-Module) in the backbone to improve representation of crater rim structures and topographic cues.
- Designed a C2PSA-LRSA module to enhance local contextual feature interaction while reducing computational cost.
- Replaced the original CIoU loss with Inner-WIoU for more flexible bounding-box regression on challenging crater features.
Main Results:
- FPW-YOLO11n achieved 78.3% Precision, 66.2% Recall, 75.1% mAP@0.5, and 50.2% mAP@0.5:0.95 on a constructed DEM-based lunar crater dataset.
- The proposed method consistently outperformed the YOLO11n baseline, demonstrating improved performance even under rigorous spatially independent evaluation.
- FPW-YOLO11n maintains a compact parameter size (2.59 M) and high inference speed, showing an improved accuracy-efficiency trade-off.
Conclusions:
- FPW-YOLO11n effectively addresses the challenges of automated lunar crater detection from DEM data, offering superior accuracy and efficiency.
- The proposed structural improvements, including FDA-Module and Inner-WIoU, enhance the detection of craters with weak rims and scale variations.
- The method provides a valuable tool for lunar geological analysis, landing-site selection, and crater catalog updates, balancing performance and computational cost.
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