SLR-Net: Lightweight and Accurate Detection of Weak Small Objects in Satellite Laser Ranging Imagery
文献类型：期刊论文
| 作者 | Zhu, Wei2,3; Hu, Jinlong3; Gong, Weiming2,3; Wang, Yong1; Zhang, Yi3 |
| 刊名 | SENSORS
 |
| 出版日期 | 2026-01-22 |
| 卷号 | 26期号:2页码:15 |
| 关键词 | Satellite Laser Ranging (SLR) small object detection lightweight network SLR-Net feature fusion |
| DOI | 10.3390/s26020732 |
| 通讯作者 | Zhang, Yi(zhangyi246@mails.ucas.ac.cn) |
| 英文摘要 | To address the challenges of insufficient efficiency and accuracy in traditional detection models caused by minute target sizes, low signal-to-noise ratios (SNRs), and feature volatility in Satellite Laser Ranging (SLR) images, this paper proposes an efficient, lightweight, and high-precision detection model. The core motivation of this study is to fundamentally enhance the model's capabilities in feature extraction, fusion, and localization for minute and blurred targets through a specifically designed network architecture and loss function, without significantly increasing the computational burden. To achieve this goal, we first design a DMS-Conv module. By employing dense sampling and channel function separation strategies, this module effectively expands the receptive field while avoiding the high computational overhead and sampling artifacts associated with traditional multi-scale methods, thereby significantly improving feature representation for faint targets. Secondly, to optimize information flow within the feature pyramid, we propose a Lightweight Upsampling Module (LUM). Integrating depthwise separable convolutions with a channel reshuffling mechanism, this module replaces traditional transposed convolutions at a minimal computational cost, facilitating more efficient multi-scale feature fusion. Finally, addressing the stringent requirements for small target localization accuracy, we introduce the MPD-IoU Loss. By incorporating the diagonal distance of bounding boxes as a geometric penalty term, this loss function provides finer and more direct spatial alignment constraints for model training, effectively boosting localization precision. Experimental results on a self-constructed real-world SLR observation dataset demonstrate that the proposed model achieves an mAP50:95 of 47.13% and an F1-score of 88.24%, with only 2.57 M parameters and 6.7 GFLOPs. Outperforming various mainstream lightweight detectors in the comprehensive performance of precision and recall, these results validate that our method effectively resolves the small target detection challenges in SLR scenarios while maintaining a lightweight design, exhibiting superior performance and practical value. |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001671528000001 |
| 出版者 | MDPI |
| 源URL | [http://ir.xao.ac.cn/handle/45760611-7/8532]  |
| 专题 | 光学天文与技术应用研究室_光学天文技术研究团组 |
| 通讯作者 | Zhang, Yi |
| 作者单位 | 1.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 2.Hubei Earthquake Agcy, Hubei Key Lab Earthquake Early Warning, Wuhan 430071, Peoples R China 3.China Earthquake Adm, Inst Seismol, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Wei,Hu, Jinlong,Gong, Weiming,et al. SLR-Net: Lightweight and Accurate Detection of Weak Small Objects in Satellite Laser Ranging Imagery[J]. SENSORS,2026,26(2):15. |
| APA | Zhu, Wei,Hu, Jinlong,Gong, Weiming,Wang, Yong,&Zhang, Yi.(2026).SLR-Net: Lightweight and Accurate Detection of Weak Small Objects in Satellite Laser Ranging Imagery.SENSORS,26(2),15. |
| MLA | Zhu, Wei,et al."SLR-Net: Lightweight and Accurate Detection of Weak Small Objects in Satellite Laser Ranging Imagery".SENSORS 26.2(2026):15. |
入库方式： OAI收割
来源：新疆天文台
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