在基于ResNet50和LSTM的离轴三镜空间光学系统中检测元素的位置
Optics express
|January 29, 2025
概括
这项研究引入了一种新的ResNet50-LSTM方法,用于在离轴光学系统中精确检测元素位置. 该方法实现了高精度和效率,节省了光学系统调整的时间和资源.
科学领域:
- 光学工程是指光学工程.
- 机器学习在光学中的应用.
背景情况:
- 精确的元素定位对于在复杂的离轴三镜空间光学系统中保持图像质量至关重要.
- 现有的元素位置检测方法在实现高精度和效率方面经常面临挑战.
研究的目的:
- 提出和评估一种新的方法,用于高精度和高效率的位置检测元素在离轴三镜空间光学系统.
- 利用深度学习技术,提高光学元件计量学中的性能.
主要方法:
- 利用ResNet50卷积神经网络从点扩展函数 (PSF) 提取高维特征向量,在不同的视野中进行高效的特征提取.
- 采用长短期记忆 (LSTM) 网络来处理这些特征向量,以获得不同图像平面的位置信息以提高精度.
- 通过三个不同的位置检测场景来评估方法:单维组件变化,多维随机变化和探测器变化.
主要成果:
- 在单维和探测器变异场景中,对于异心率,实现了100%的检测精度,比10μm更好.
- 在多维随机变异场景中,在倾斜时达到94%的检测准确度,比10秒更好.
- 证明该方法通过单次计算获得元素位置,接近最终结果,并大大减少调整时间和资源.
结论:
- 拟议的ResNet50-LSTM方法为离轴三镜空间光学系统中元素位置检测提供了准确有效的解决方案.
- 这种基于深度学习的方法提高了光学元件计量学的精度和效率,有助于提高成像质量和简化系统对齐.
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