YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang’e-6 Landing Area
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 e...
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2025-01-01
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| author | Jing Nan Yexin Wang Kaichang Di Bin Xie Chenxu Zhao Biao Wang Shujuan Sun Xiangjin Deng Hong Zhang Ruiqing Sheng |
| author_facet | Jing Nan Yexin Wang Kaichang Di Bin Xie Chenxu Zhao Biao Wang Shujuan Sun Xiangjin Deng Hong Zhang Ruiqing Sheng |
| author_sort | Jing Nan |
| collection | DOAJ |
| description | 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 × 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. |
| format | Article |
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| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-4e98c3f286894e1d957ee8152413c6d02025-01-10T13:21:20ZengMDPI AGSensors1424-82202025-01-0125124310.3390/s25010243YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang’e-6 Landing AreaJing Nan0Yexin Wang1Kaichang Di2Bin Xie3Chenxu Zhao4Biao Wang5Shujuan Sun6Xiangjin Deng7Hong Zhang8Ruiqing Sheng9State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, ChinaBeijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, ChinaBeijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, ChinaBeijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, ChinaThe 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 × 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.https://www.mdpi.com/1424-8220/25/1/243lunar surfaceCE-6 landing areadigital orthophoto mapimpact craterautomatic detectionYou Only Look Once-v8 |
| spellingShingle | Jing Nan Yexin Wang Kaichang Di Bin Xie Chenxu Zhao Biao Wang Shujuan Sun Xiangjin Deng Hong Zhang Ruiqing Sheng YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang’e-6 Landing Area Sensors lunar surface CE-6 landing area digital orthophoto map impact crater automatic detection You Only Look Once-v8 |
| title | YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang’e-6 Landing Area |
| title_full | YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang’e-6 Landing Area |
| title_fullStr | YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang’e-6 Landing Area |
| title_full_unstemmed | YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang’e-6 Landing Area |
| title_short | YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang’e-6 Landing Area |
| title_sort | yolov8 lcnet an improved yolov8 automatic crater detection algorithm and application in the chang e 6 landing area |
| topic | lunar surface CE-6 landing area digital orthophoto map impact crater automatic detection You Only Look Once-v8 |
| url | https://www.mdpi.com/1424-8220/25/1/243 |
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