伪样本生成和自我监督的框架用于红外模糊和小目标检测
Jinxin Guo1, Weida Zhan1, Dehua Huo1
1School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
这项研究引入了一种新的方法,通过模拟物理降解过程来生成现实的红外模拟数据. 这种方法改进了深度学习模型,用于在现实场景中检测模糊和小目标.
科学领域:
- 计算机视觉 计算机视觉
- 红外成像技术 红外成像技术
- 机器学习 机器学习
背景情况:
- 红外模糊和小目标检测对于远程传感至关重要.
- 对于这项任务的深度学习受到缺乏真实注释数据的限制.
- 当前的合成数据方法并不准确地反映现实世界的红外成像物理.
研究的目的:
- 为红外目标检测开发一种新的伪样本生成范式.
- 为了解决现有的合成数据生成方法的局限性.
- 提高红外目标检测模型的性能和通用性.
主要方法:
- 基于物理的降解建模,以解目标和背景过程.
- 为可靠的退化建模提供信息忠实度优化.
- 在线基于网格的高阶约束 (语义,结构,灰度) 用于数据集生成.
- 一个自我监督的检测框架,具有自定义的损失功能和评估指标.
主要成果:
- 与现有方法相比,生成的合成数据显示出更高的真实性.
- 拟议的方法显著提高了各种探测器的概括性能.
- 与基线模型相比,在真实数据上实现了更高的检测准确度.
结论:
- 基于物理学的伪样本生成范式有效地创建了高可靠性的红外模拟数据.
- 这种方法克服了红外暗和小目标检测中的数据稀缺问题.
- 该方法为改善现实世界的红外传感应用提供了一个有希望的解决方案.
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