Multi-Scale Efficient Fusion Network for Lunar Impact Crater Detection

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Abstract
Lunar Impact Craters are geologic formations that contain vestiges of the Moon’s historical evolution. Detecting and analysing them at the lunar surface is a crucial issue. This paper proposed an improved YOLO v11 model called YOLO v11_MSEF. The enhanced interlayer feature correlation (EFC) mechanism adds the channel attention model and local attention model to let the YOLO v11 enhance expressive power for multi-scale feature fusion, which is named YOLO v11_MSEF. The datasets are set as 9500 labelled targets for training and 2500 for validation, which were applied in this project. The YOLO v11_MSEF mode achieves a precision performance of 70.40%, which is a significant improvement over the 68.90% achieved by the YOLO v11, the remaining performance parameters also demonstrate comparable or superior results.