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Crater-MASN: A Multi-Scale Adaptive Semantic Network for Efficient Crater Detection.

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Title: Crater-MASN: A Multi-Scale Adaptive Semantic Network for Efficient Crater Detection.
Authors: Yu, Ruiqi1 (AUTHOR), Xu, Zhijing1 (AUTHOR) zjxu@shmtu.edu.cn
Source: Remote Sensing. Sep2025, Vol. 17 Issue 18, p3139. 27p.
Subjects: Lunar craters, Planetary science, Impact craters, Feature extraction, Semantic networks (Information theory), Software frameworks, Mechanical efficiency
Abstract: Highlights: What are the main findings? A lightweight multi-scale framework, Crater-MASN, is proposed to balance high accuracy with computational efficiency. A novel training and post-processing pipeline enables robust detection in dense, nested regions and demonstrates an exceptional capability for discovering previously uncatalogued craters. What is the implication of the main finding? Crater-MASN provides a scalable and efficient tool for planetary scientists to perform large-scale, high-precision crater cataloging and planetary surface analysis. The model's proven ability to identify uncatalogued craters demonstrates its significant potential for scientific discovery and the completion of existing lunar databases. Automatic crater detection is crucial for planetary science, but still faces several long-standing challenges. The morphological characteristics of craters exhibit significant variability; combined with complex lighting conditions, this makes feature extraction difficult, especially for small or severely degraded features. These difficulties are further compounded by incomplete ground truth annotations, which limit the effectiveness of supervised learning. In addition, achieving a balance between detection accuracy and computational efficiency remains a critical bottleneck, especially in large-scale planetary surveys. Traditional postprocessing algorithms also often struggle to resolve complex spatial hierarchies in densely cratered regions, leading to substantial omissions and misclassifications. To address these interrelated challenges, we propose Crater-MASN, a lightweight adaptive detection framework specifically designed for lunar crater analysis. The architecture employs a compact GhostNet backbone to balance efficiency and accuracy, while enhancing multi-scale feature representation through a novel bidirectional integration and fusion module (BIFM) to better capture the morphological diversity of craters. To mitigate the impact of incomplete annotations, we introduce an adaptive semantic contrastive sampling (ASCS) mechanism which dynamically mines unlabeled craters through semantic clustering and contrastive learning. Additionally, we design the hierarchical soft NMS (H-SoftNMS) algorithm, a geometry-aware postprocessing method that selectively suppresses non-hierarchical overlaps to preserve nested craters, thereby achieving more accurate crater retention in dense regions. Experiments on a dedicated lunar crater dataset demonstrate the effectiveness of Crater-MASN. The model achieves an mAP50 of 91.0% with only 2.1 million parameters. When combined with H-SoftNMS, it achieves a recall rate of 95.0% and new discovery rate P NDR of 89.6%. These results highlight the potential of Crater-MASN as a scalable and reliable tool for high-precision crater cataloging and planetary surface analysis. [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: Crater-MASN: A Multi-Scale Adaptive Semantic Network for Efficient Crater Detection.
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  Data: <searchLink fieldCode="AR" term="%22Yu%2C+Ruiqi%22">Yu, Ruiqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Zhijing%22">Xu, Zhijing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zjxu@shmtu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Sep2025, Vol. 17 Issue 18, p3139. 27p.
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  Data: <searchLink fieldCode="DE" term="%22Lunar+craters%22">Lunar craters</searchLink><br /><searchLink fieldCode="DE" term="%22Planetary+science%22">Planetary science</searchLink><br /><searchLink fieldCode="DE" term="%22Impact+craters%22">Impact craters</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Semantic+networks+%28Information+theory%29%22">Semantic networks (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Software+frameworks%22">Software frameworks</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+efficiency%22">Mechanical efficiency</searchLink>
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  Label: Abstract
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  Data: Highlights: What are the main findings? A lightweight multi-scale framework, Crater-MASN, is proposed to balance high accuracy with computational efficiency. A novel training and post-processing pipeline enables robust detection in dense, nested regions and demonstrates an exceptional capability for discovering previously uncatalogued craters. What is the implication of the main finding? Crater-MASN provides a scalable and efficient tool for planetary scientists to perform large-scale, high-precision crater cataloging and planetary surface analysis. The model's proven ability to identify uncatalogued craters demonstrates its significant potential for scientific discovery and the completion of existing lunar databases. Automatic crater detection is crucial for planetary science, but still faces several long-standing challenges. The morphological characteristics of craters exhibit significant variability; combined with complex lighting conditions, this makes feature extraction difficult, especially for small or severely degraded features. These difficulties are further compounded by incomplete ground truth annotations, which limit the effectiveness of supervised learning. In addition, achieving a balance between detection accuracy and computational efficiency remains a critical bottleneck, especially in large-scale planetary surveys. Traditional postprocessing algorithms also often struggle to resolve complex spatial hierarchies in densely cratered regions, leading to substantial omissions and misclassifications. To address these interrelated challenges, we propose Crater-MASN, a lightweight adaptive detection framework specifically designed for lunar crater analysis. The architecture employs a compact GhostNet backbone to balance efficiency and accuracy, while enhancing multi-scale feature representation through a novel bidirectional integration and fusion module (BIFM) to better capture the morphological diversity of craters. To mitigate the impact of incomplete annotations, we introduce an adaptive semantic contrastive sampling (ASCS) mechanism which dynamically mines unlabeled craters through semantic clustering and contrastive learning. Additionally, we design the hierarchical soft NMS (H-SoftNMS) algorithm, a geometry-aware postprocessing method that selectively suppresses non-hierarchical overlaps to preserve nested craters, thereby achieving more accurate crater retention in dense regions. Experiments on a dedicated lunar crater dataset demonstrate the effectiveness of Crater-MASN. The model achieves an mAP50 of 91.0% with only 2.1 million parameters. When combined with H-SoftNMS, it achieves a recall rate of 95.0% and new discovery rate P NDR of 89.6%. These results highlight the potential of Crater-MASN as a scalable and reliable tool for high-precision crater cataloging and planetary surface analysis. [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/rs17183139
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      – Code: eng
        Text: English
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        PageCount: 27
        StartPage: 3139
    Subjects:
      – SubjectFull: Lunar craters
        Type: general
      – SubjectFull: Planetary science
        Type: general
      – SubjectFull: Impact craters
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Semantic networks (Information theory)
        Type: general
      – SubjectFull: Software frameworks
        Type: general
      – SubjectFull: Mechanical efficiency
        Type: general
    Titles:
      – TitleFull: Crater-MASN: A Multi-Scale Adaptive Semantic Network for Efficient Crater Detection.
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            NameFull: Yu, Ruiqi
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            NameFull: Xu, Zhijing
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            – D: 15
              M: 09
              Text: Sep2025
              Type: published
              Y: 2025
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