Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalo...Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalogs.In this work,we integrate Digital Elevation Model(DEM)data to construct a high-quality dataset enriched with slope information,enabling a detailed analysis of crater features and effectively improving detection performance in complex terrains and low-contrast areas.Based on this foundation,we propose a novel two-stage detection network,MSFNet,which leverages multi-scale adaptive feature fusion and multisize ROI pooling to enhance the recognition of craters across various scales.Experimental results demonstrate that MSFNet achieves an F1 score of 74.8%on Test Region1 and a recall rate of 87%for craters with diameters larger than 2 km.Moreover,it shows exceptional performance in detecting sub-kilometer craters by successfully identifying a large number of high-confidence,previously unlabeled targets with a low false detection rate confirmed through manual review.This approach offers an efficient and reliable deep learning solution for lunar impact crater detection.展开更多
Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due ...Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.展开更多
The development of guidance technology has made it possible for the earth penetration weapons(EPWs)to impact the target repeatedly at a close range. To investigative the damage of single and sequential strike induced ...The development of guidance technology has made it possible for the earth penetration weapons(EPWs)to impact the target repeatedly at a close range. To investigative the damage of single and sequential strike induced by the EPWs, experimental and numerical investigations are carried out in this paper.Firstly, a series of sequential explosion tests are conducted to provide the basic data of the crater size.Then, a numerical model is established to simulate the damage effects of sequential explosions using the meshfree method of Smoothed particle Galerkin. The effectiveness of numerical model is verified by comparison with the experimental results. Finally, based on dimensional analysis, several empirical formulas for describing the crater size are presented, including the conical crater diameter and the conical crater depth of the single explosion, the conical crater area and the joint depth of the secondary explosion. The formula for the single explosion expresses the relationship between the aspect ratio of the charge ranging from 3 to 7, the dimensionless buried depth ranging from 2 to 14 and the crater size. The formula for the secondary explosion expresses the relationship between the relative position of the two explosions and the crater size. All of data can provide reference for the design of protective structures.展开更多
基金National Natural Science Foundation of China(12103020,12363009)Natural Science Foundation of Jiangxi Province(20224BAB211011)+1 种基金Open Project Program of State Key Laboratory of Lunar and Planetary Sciences(Macao University of Science and Technology)(Macao FDCT grant No.002/2024/SKL)Youth Talent Project of Science and Technology Plan of Ganzhou(2022CXRC9191,2023CYZ26970)。
文摘Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalogs.In this work,we integrate Digital Elevation Model(DEM)data to construct a high-quality dataset enriched with slope information,enabling a detailed analysis of crater features and effectively improving detection performance in complex terrains and low-contrast areas.Based on this foundation,we propose a novel two-stage detection network,MSFNet,which leverages multi-scale adaptive feature fusion and multisize ROI pooling to enhance the recognition of craters across various scales.Experimental results demonstrate that MSFNet achieves an F1 score of 74.8%on Test Region1 and a recall rate of 87%for craters with diameters larger than 2 km.Moreover,it shows exceptional performance in detecting sub-kilometer craters by successfully identifying a large number of high-confidence,previously unlabeled targets with a low false detection rate confirmed through manual review.This approach offers an efficient and reliable deep learning solution for lunar impact crater detection.
基金supported by the National Natural Science Foundation of China(61210012)
文摘Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.
文摘The development of guidance technology has made it possible for the earth penetration weapons(EPWs)to impact the target repeatedly at a close range. To investigative the damage of single and sequential strike induced by the EPWs, experimental and numerical investigations are carried out in this paper.Firstly, a series of sequential explosion tests are conducted to provide the basic data of the crater size.Then, a numerical model is established to simulate the damage effects of sequential explosions using the meshfree method of Smoothed particle Galerkin. The effectiveness of numerical model is verified by comparison with the experimental results. Finally, based on dimensional analysis, several empirical formulas for describing the crater size are presented, including the conical crater diameter and the conical crater depth of the single explosion, the conical crater area and the joint depth of the secondary explosion. The formula for the single explosion expresses the relationship between the aspect ratio of the charge ranging from 3 to 7, the dimensionless buried depth ranging from 2 to 14 and the crater size. The formula for the secondary explosion expresses the relationship between the relative position of the two explosions and the crater size. All of data can provide reference for the design of protective structures.
