YOLO-GRBI:在复杂的轨道环境中,用于非合作空间目标的增强轻量级探测器
Zimo Zhou1, Shuaiqun Wang1, Xinyao Wang2
1School of Information Engineering, Shanghai Maritime University, 1550 Haigang Avenue, Pudong New Area, Shanghai 201306, China.
Entropy (Basel, Switzerland)
|September 27, 2025
概括
这项研究介绍了YOLO-GRBI,这是一种有效的AI模型,用于检测太空目标. 它显著提高了准确性,并减少了自主航天任务和情境意识的计算负载.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 自主轨道服务和空间局势感知 (SSA) 需要强大的非合作空间目标检测.
- 现有的物体检测方法面临着有限的内置计算资源和复杂的空间成像环境的挑战,导致小的,模糊的目标的准确性较低.
研究的目的:
- 开发一个增强的物体检测网络 (YOLO-GRBI),在太空应用中平衡高精度和计算效率.
- 解决当前框架在杂空间图像中检测具有低信息率的小目标方面的局限性.
主要方法:
- 实施了重定量化的ELAN骨干,以改善功能重复使用和梯度流动.
- 引入了BiFormer和C2f-iAFF模块,以提高突出目标的注意力并减少检测错误.
- 在子中集成GSConv和VoV-GSCSP模块以优化计算冗余性和保护信息.
- 利用焦点损失进行分类和信心预测,以处理类不平衡.
主要成果:
- 与基线YOLOv8n相比,YOLO-GRBI在定制航天器数据集上表现出更高的性能.
- 实现了mAP@0.5的4.9%增长和mAP@0.5:0.95.5的6.0%增长.
- 成功地降低了模型复杂性和推断延迟,同时保持了高检测准确性.
结论:
- 在太空应用中,YOLO-GRBI在高效和准确的非合作空间目标检测方面取得了重大进展.
- 拟议的网络有效地解决了有限资源和复杂的空间成像条件的挑战.
- 这项工作有助于提高空间服务的自主能力,提高空间局势意识.
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