红外物体检测网络 (IDDNet):基于多层次的核聚变脱气网络的红外物体检测网络.
Shizun Sun1, Shuo Han1, Junwei Xu1
1School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了IDDNet,这是一个用于在雾中检测红外物体的轻量级网络. IDDNet提高了图像清晰度和物体可见性,提高了检测准确度和恶劣天气条件下的实时性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 雾条件降低了红外图像质量,减少了对比度和细节.
- 图像质量下降会对对象检测准确性和实时处理产生负面影响.
研究的目的:
- 开发用于雾环境的轻量级红外物体检测网络.
- 通过解决雾干扰来提高检测准确度和实时性能.
主要方法:
- 拟议的IDDNet,一个集成多规模聚变脱 (MSFD) 的网络.
- 整合了一个专门的除损失功能 (DhLoss).
- 利用双向的偏振自我注意力,加权的双向特征金字塔网络和多尺度检测层.
主要成果:
- 在公共数据集上,IDDNet实现了89.4%的精度和83.9%的AP.
- 证明了卓越的准确性,处理速度和概括能力.
- 在雾环境中展示了强大的检测性能.
结论:
- IDDNet有效地减轻了红外图像中的雾干扰.
- 拟议的网络提供了一个轻量级和高效的解决方案,用于在恶劣天气中检测物体.
- IDDNet 提高了现实世界的应用程序的准确性和稳定性.
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