YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang'e-6 Landing Area

被引:0
|
作者
Nan, Jing [1 ,2 ]
Wang, Yexin [1 ]
Di, Kaichang [1 ,3 ]
Xie, Bin [1 ,2 ]
Zhao, Chenxu [1 ,2 ]
Wang, Biao [1 ,2 ]
Sun, Shujuan [4 ]
Deng, Xiangjin [5 ]
Zhang, Hong [5 ]
Sheng, Ruiqing [5 ]
机构
[1] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100101, Peoples R China
[3] Chinese Acad Sci, Ctr Excellence Comparat Planetol, Hefei 230026, Peoples R China
[4] Chengdu Univ, Sch Architecture & Civil Engn, Chengdu 610106, Peoples R China
[5] China Acad Space Technol, Beijing Inst Spacecraft Syst Engn, Beijing 100094, Peoples R China
关键词
lunar surface; CE-6 landing area; digital orthophoto map; impact crater; automatic detection; You Only Look Once-v8; MARTIAN IMPACT CRATERS; LUNAR;
D O I
10.3390/s25010243
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The Chang'e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole-Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed. The model first incorporated a Partial Self-Attention (PSA) mechanism at the end of the Backbone, allowing the model to enhance global perception and reduce missed detections with a low computational cost. Then, a Gather-and-Distribute mechanism (GD) was integrated into the Neck, enabling the model to fully fuse multi-level feature information and capture global information, enhancing the model's ability to detect impact craters of various sizes. The experimental results showed that the YOLOv8-LCNET model performs well in the impact crater detection task, achieving 87.7% Precision, 84.3% Recall, and 92% AP, which were 24.7%, 32.7%, and 37.3% higher than the original YOLOv8 model. The improved YOLOv8 model was then used for automatic crater detection in the CE-6 landing area (246 km x 135 km, with a DOM resolution of 3 m/pixel), resulting in a total of 770,671 craters, ranging from 13 m to 19,882 m in diameter. The analysis of this impact crater catalogue has provided critical support for landing site selection and characterization of the CE-6 mission and lays the foundation for future lunar geological studies.
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页数:16
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