一个波形重新校准的半监督网络,用于在数据稀缺的情况下进行红外小目标检测
Cheng Jiang1, Jingwen Ma2, Xinpeng Zhang2
1Beijing Institute of Space Mechanics & Electricity, Beijing 100094, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种新的波段再校准半监督网络 (WRSSNet),用于红外小目标检测. 通过合成数据和先进的功能融合技术,WRSSNet有效地提高了检测准确度,并减少了虚假报警.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 红外小目标检测面临着小目标尺寸,低对比度和有限的注释数据的挑战.
- 现有的方法在数据稀缺和有效利用未标记的红外图像方面扎.
研究的目的:
- 为改进红外小目标检测提出一个波波重新校准的半监督网络 (WRSSNet).
- 通过整合合成数据增强和半监督学习来解决数据稀缺问题.
- 增强特征表示,以突出弱点目标.
主要方法:
- 开发了WRSSNet,集成合成数据增强,特征重建和半监督学习.
- 利用改进的CycleGAN将可见光图像转换为伪红外图像,扩展训练数据.
- 设计了一个波波增强通道重新校准和融合 (WECRF) 模块,具有波波分解和注意力机制,用于多尺度特征融合.
主要成果:
- 在NUAA-SIRST和IRSTD-1K数据集上,WRSSNet表现出卓越的检测准确性.
- 与最先进的方法相比,实现了明显较低的错误报警率.
- 保持较低的计算复杂性,使其对实际应用有效.
结论:
- 拟议的WRSSNet有效地克服了红外小目标检测方面的挑战.
- 合成数据增强和WECRF模块对于提高性能至关重要.
- 在有限的监督下,WRSSNet提供了一个有前途的解决方案,用于在有限的监督下稳健有效地检测红外小目标.
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