Identification of small impact craters in Chang’e-4 landing areas using a new multi-scale fusion crater detection algorithm

Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates, among other functions. However, the morphological characteristics of these micro impact craters are not obvious and they are numerous, resulting in low detection accuracy by deep learning models. Therefore, we proposed a new multi-scale fusion crater detection algorithm (MSF-CDA) based on the YOLO11 to improve the accuracy of lunar impact crater detection, especially for small craters with a diameter of <1 km. Using the images taken by the LROC (Lunar Reconnaissance Orbiter Camera) at the Chang’e-4 (CE-4) landing area, we constructed three separate datasets for craters with diameters of 0–70 m, 70–140 m, and >140 m. We then trained three submodels separately with these three datasets. Additionally, we designed a slicing–amplifying–slicing strategy to enhance the ability to extract features from small craters. To handle redundant predictions, we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels. Finally, our new MSF-CDA method achieved high detection performance, with the Precision, Recall, and F1 score having values of 0.991, 0.987, and 0.989, respectively, perfectly addressing the problems induced by the lesser features and sample imbalance of small craters. Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations. This strategy can also be used to detect other small objects with lesser features and sample imbalance problems. We detected approximately 500,000 impact craters in an area of approximately 214 km² around the CE-4 landing area. By statistically analyzing the new data, we updated the distribution function of the number and diameter of impact craters. Finally, we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.