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DBYOLO: Dual-Backbone YOLO Network for Lunar Crater Detection.

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Title: DBYOLO: Dual-Backbone YOLO Network for Lunar Crater Detection.
Authors: Liu, Yawen1 (AUTHOR), Chen, Fukang1,2 (AUTHOR), Qiu, Denggao1,2 (AUTHOR), Liu, Wei1,2 (AUTHOR), Yan, Jianguo2 (AUTHOR) jgyan@whu.edu.cn
Source: Remote Sensing. Oct2025, Vol. 17 Issue 19, p3377. 27p.
Subjects: Lunar craters, Digital elevation models, Observations of the Moon, Artificial neural networks, Feature extraction, Lunar Reconnaissance Orbiter (Spacecraft), Machine learning
Abstract: Highlights: What are the main findings? Designed a lightweight dual-backbone network to extract texture and edge features from LROC CCD images and terrain depth features from DTM data. Proposed a feature fusion module based on attention mechanisms to dynamically integrate features from multi-source data at different scales. What are the implications of the main findings? mAP50 improved by 3.1% compared to the baseline model. The model's prediction plot better fits the ground truth compared to other mainstream models. Craters are among the most prominent and significant geomorphological features on the lunar surface. The complex and variable environment of the lunar surface, which is characterized by diverse textures, lighting conditions, and terrain variations, poses significant challenges to existing crater detection methods. To address these challenges, this study introduces DBYOLO, an innovative deep learning framework designed for lunar crater detection, leveraging a dual-backbone feature fusion network, with two key innovations. The first innovation is a lightweight dual-backbone network that processes Lunar Reconnaissance Orbiter Camera (LROC) CCD images and Digital Terrain Model (DTM) data separately, extracting texture and edge features from CCD images and terrain depth features from DTM data. The second innovation is a feature fusion module with attention mechanisms that is used to dynamically integrate multi-source data, enabling the efficient extraction of complementary information from both CCD images and DTM data, enhancing crater detection performance in complex lunar surface environments. Experimental results demonstrate that DBYOLO, with only 3.6 million parameters, achieves a precision of 77.2%, recall of 70.3%, mAP50 of 79.4%, and mAP50-95 of 50.4%, representing improvements of 3.1%, 1.8%, 3.1%, and 2.6%, respectively, over the baseline model before modifications. This showcases an overall performance enhancement, providing a new solution for lunar crater detection and offering significant support for future lunar exploration efforts. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: DBYOLO: Dual-Backbone YOLO Network for Lunar Crater Detection.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Yawen%22">Liu, Yawen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Fukang%22">Chen, Fukang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Denggao%22">Qiu, Denggao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Wei%22">Liu, Wei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Jianguo%22">Yan, Jianguo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> jgyan@whu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Oct2025, Vol. 17 Issue 19, p3377. 27p.
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  Data: <searchLink fieldCode="DE" term="%22Lunar+craters%22">Lunar craters</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+elevation+models%22">Digital elevation models</searchLink><br /><searchLink fieldCode="DE" term="%22Observations+of+the+Moon%22">Observations of the Moon</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Lunar+Reconnaissance+Orbiter+%28Spacecraft%29%22">Lunar Reconnaissance Orbiter (Spacecraft)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Label: Abstract
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  Data: Highlights: What are the main findings? Designed a lightweight dual-backbone network to extract texture and edge features from LROC CCD images and terrain depth features from DTM data. Proposed a feature fusion module based on attention mechanisms to dynamically integrate features from multi-source data at different scales. What are the implications of the main findings? mAP50 improved by 3.1% compared to the baseline model. The model's prediction plot better fits the ground truth compared to other mainstream models. Craters are among the most prominent and significant geomorphological features on the lunar surface. The complex and variable environment of the lunar surface, which is characterized by diverse textures, lighting conditions, and terrain variations, poses significant challenges to existing crater detection methods. To address these challenges, this study introduces DBYOLO, an innovative deep learning framework designed for lunar crater detection, leveraging a dual-backbone feature fusion network, with two key innovations. The first innovation is a lightweight dual-backbone network that processes Lunar Reconnaissance Orbiter Camera (LROC) CCD images and Digital Terrain Model (DTM) data separately, extracting texture and edge features from CCD images and terrain depth features from DTM data. The second innovation is a feature fusion module with attention mechanisms that is used to dynamically integrate multi-source data, enabling the efficient extraction of complementary information from both CCD images and DTM data, enhancing crater detection performance in complex lunar surface environments. Experimental results demonstrate that DBYOLO, with only 3.6 million parameters, achieves a precision of 77.2%, recall of 70.3%, mAP50 of 79.4%, and mAP50-95 of 50.4%, representing improvements of 3.1%, 1.8%, 3.1%, and 2.6%, respectively, over the baseline model before modifications. This showcases an overall performance enhancement, providing a new solution for lunar crater detection and offering significant support for future lunar exploration efforts. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17193377
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      – Code: eng
        Text: English
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        PageCount: 27
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      – SubjectFull: Lunar craters
        Type: general
      – SubjectFull: Digital elevation models
        Type: general
      – SubjectFull: Observations of the Moon
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Lunar Reconnaissance Orbiter (Spacecraft)
        Type: general
      – SubjectFull: Machine learning
        Type: general
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      – TitleFull: DBYOLO: Dual-Backbone YOLO Network for Lunar Crater Detection.
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            NameFull: Liu, Yawen
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              M: 10
              Text: Oct2025
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              Y: 2025
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