SCCA-YOLO: Spatial Channel Fusion and Context-Aware YOLO for Lunar Crater Detection.
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| Title: | SCCA-YOLO: Spatial Channel Fusion and Context-Aware YOLO for Lunar Crater Detection. |
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| Authors: | Tang, Jiahao1 (AUTHOR), Gu, Boyuan2 (AUTHOR), Li, Tianyou1,3 (AUTHOR), Lu, Ying-Bo1,3 (AUTHOR) lyb@sdu.edu.cn |
| Source: | Remote Sensing. Jul2025, Vol. 17 Issue 14, p2380. 19p. |
| Subjects: | Lunar craters, Feature extraction, Topographic maps, Remote sensing, Object recognition (Computer vision), Image processing, Geological research |
| Abstract: | Lunar crater detection plays a crucial role in geological analysis and the advancement of lunar exploration. Accurate identification of craters is also essential for constructing high-resolution topographic maps and supporting mission planning in future lunar exploration efforts. However, lunar craters often suffer from insufficient feature representation due to their small size and blurred boundaries. In addition, the visual similarity between craters and surrounding terrain further exacerbates background confusion. These challenges significantly hinder detection performance in remote sensing imagery and underscore the necessity of enhancing both local feature representation and global semantic reasoning. In this paper, we propose a novel Spatial Channel Fusion and Context-Aware YOLO (SCCA-YOLO) model built upon the YOLO11 framework. Specifically, the Context-Aware Module (CAM) employs a multi-branch dilated convolutional structure to enhance feature richness and expand the local receptive field, thereby strengthening the feature extraction capability. The Joint Spatial and Channel Fusion Module (SCFM) is utilized to fuse spatial and channel information to model the global relationships between craters and the background, effectively suppressing background noise and reinforcing feature discrimination. In addition, the improved Channel Attention Concatenation (CAC) strategy adaptively learns channel-wise importance weights during feature concatenation, further optimizing multi-scale semantic feature fusion and enhancing the model's sensitivity to critical crater features. The proposed method is validated on a self-constructed Chang'e 6 dataset, covering the landing site and its surrounding areas. Experimental results demonstrate that our model achieves an m A P 0.5 of 96.5% and an m A P 0.5 : 0.95 of 81.5%, outperforming other mainstream detection models including the YOLO family of algorithms. These findings highlight the potential of SCCA-YOLO for high-precision lunar crater detection and provide valuable insights into future lunar surface analysis. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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