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在热适应的深层物体探测模型上增加大气流的影响
Engin Uzun1,2, Erdem Akagündüz3
1Department of Image Processing and Computer Vision Technologies, Aselsan Inc., Ankara, Turkey. enginuzun@aselsan.com.
Scientific reports
|March 23, 2025
概括
大气动荡会降低对象检测的性能. 特定于流的图像增强显著提高了深度学习模型的准确性和稳定性,甚至提高了清晰图像的性能.
科学领域:
- 计算机视觉 计算机视觉
- 大气光学是大气光学.
- 机器学习 机器学习
背景情况:
- 大气动荡会扭曲光学图像,影响物体检测模型的性能.
- 空气折射率的变化会导致光散射,导致模糊和几何扭曲.
研究的目的:
- 评估流图像增强,以增强对象检测模型.
- 在大气动荡下评估深度学习模型的稳定性.
主要方法:
- 采用了三个流模拟器 (几何,基于Zernike,P2S) 来创建流数据集.
- 经过训练和测试的RTMDet-x,DINO-4scale和YOLOv8-x模型,具有和没有流增强.
- 在流和非流测试集上评估模型性能.
主要成果:
- 针对流的特定增强可以大大提高对象检测的准确性和稳定性.
- 与非增强模型相比,增强模型在扭曲图像上表现更好.
- 即使在非流测试数据上也观察到性能增长.
结论:
- 流图像增强是一种有效的策略,可以改善在恶劣大气条件下对象检测.
- 深度学习模型从专门的增强中受益,以克服流引起的图像退化.
- 拟议的增强技术增强了模型概括能力.
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