Lunar Dome Detection Method Based on Few-Shot Object Detection

Abstract

Abstract Lunar domes have always been one of the important windows to understand lunar volcanic activities, but traditional geological dome identification methods are costly. This study attempts to establish an automatic identification method for lunar volcanic domes through FSOD (Few-Shot Object Detection). Since our previous research has been trying to automatically identify lunar volcanic hills using traditional recognition algorithms, our team will try Few-Shot Object Detection this time. In this study, the researchers first obtained the dome coordinates from the list of known lunar domes and intercepted the data this research need from the corresponding coordinates on the DEM moon images. Subsequently, this research used the existing data to train six traditional recognition algorithms and FSOD and compared their performance to verify the feasibility of this study. This research use AP50 and AP75 to evaluate the performance of the model. Finally, this research found that the AP50 of FSOD can reach 0.76, and the AP75 can reach 0.43. However, although FSOD does perform better than other traditional identification methods, this research believe its accuracy is still far from our expectations. Admittedly, compared with YOLO v5, the performance of FSOD has not been significantly improved, but this study still verifies that it is feasible to apply FSOD to the identification of lunar volcanic domes.