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YOLO-MARS用于红外目标检测:朝着近距离空间发展
Bohan Liu1,2, Yeteng Han1,2, Pengxi Liu1
1Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究介绍了YOLO-MARS,这是一款用于近太空环境的高效红外目标检测模型. 它提高了对小目标的检测准确度,并通过新型模块和扩展数据集减少了背景噪声.
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:
- 在近距离太空中红外目标检测面临诸如尺度变化,噪音和低对比度等挑战.
- 现有的模型在这些环境中与小目标和复杂的背景作斗争.
研究的目的:
- 为近距离太空应用提出一个高效准确的红外目标检测模型.
- 为了解决传统下方采样和特征融合在红外图像中的局限性.
- 为近距离太空目标检测培训创建一个更强大的数据集.
主要方法:
- 开发了YOLO-MARS,结合了空间到深度 (SPD) 卷积,组合多头自我注意 (GMHSA) 和光适应空间特征融合 (LASFF) 模块.
- 将近距离空间图像与HIT-UAV数据集集成,以创建NS-HIT数据集.
- 对YOLOv8进行模型性能评估,重点关注准确性和计算复杂性.
主要成果:
- 与YOLOv8.5相比,YOLO-MARS在mAP@0.5中实现了5.4%的增加.
- 模型复杂度仅增加了10%,精度得到了显著的改进.
- 增强检测小目标和抑制背景噪声的能力.
结论:
- 对于近太空红外目标检测,YOLO-MARS提供了一种高效可靠的解决方案.
- 拟议的模型有效地处理尺度变化和复杂的背景噪声.
- 新的NS-HIT数据集有助于为这个领域培养更强大的检测模型.
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