一种基于模式对齐和融合的全天候遥感对象检测方法
Yongjun Qi1, Shaohua Yang2, Jiahao Chen3
1School of Computer Science and Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了全天候跨模态遥感物体检测的新框架,显著提高了恶劣天气和可见光,红外和SAR等不同数据类型的性能.
科学领域:
- 遥感技术
- 计算机视觉
- 人工智能
背景情况:
- 跨模态遥感对象检测面临由于模态差异和恶劣天气特征降解的挑战.
- 现有的方法难以全天候可靠的检测,限制了监视和侦察的应用.
研究的目的:
- 开发一个强大的跨模态遥感物体检测的新框架.
- 解决可见光,红外和合成光圈雷达 (SAR) 数据的模式差异和特征降解的挑战.
- 提高24小时监视,军事侦察和应急行动的性能.
主要方法:
- 一个多尺度的特征提取模块使用等级卷积架构来捕获细节和上下文.
- 一个创新的功能交互模块与交叉关注远程依赖和适应性噪音抑制.
- 一个功能校正融合模块用于空间对齐和跨模式的全球功能一致性.
主要成果:
- 该框架在具有挑战性的数据集上获得了最先进的平均精度 (mAP):66.3% (LLVIP),58.6% (OGSOD) 和71.7% (无人机车辆).
- 与现有方法相比显著改进,特别是在模式差异或极端天气条件的场景中.
- 在可见光,红外和SAR模式中验证了有效性.
结论:
- 拟议的框架为在具有挑战性的条件下进行跨模式物体检测提供了强有力的解决方案.
- 在关键的24小时应用中, 推动了遥感物体检测的技术前沿.
- 对于需要全天候可靠监视和侦察的关键任务提供实用价值.
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