一个基于卷积神经网络的框架,通过斑点移位分析进行质量控制
Hamed Sabahno1, Davood Khodadad2
1Department of Applied Physics and Electronics, Umeå University, Umeå, 90187, Sweden. hamed.sabahno@stat.lu.se.
Scientific reports
|November 10, 2025
概括
本研究介绍了一种新的算法,用于精确测量光斑位移,使用基于富里埃的图像注册和卷积神经网络 (CNN). 该方法可以准确地检测多个物体的运动,从而提高光学计量学中的质量控制.
科学领域:
- 光学计量学 在光学计量学
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 斑点计量学依赖于分析激光斑点图案以进行精确的测量.
- 精确量化斑点位移对于检测物体缺陷和变形至关重要.
- 现有的方法在处理复杂的多方向运动方面面临挑战.
研究的目的:
- 开发一种先进的算法,用于准确测量斑点位移.
- 集成卷积神经网络 (CNN) 以优化网格重叠计算.
- 为了同时检测物体部分的多个转换运动.
主要方法:
- 将图像细分成重叠的网格.
- 基于福里埃的图像注册用于位移量化.
- 卷积神经网络 (CNN) 用于优化网格重叠参数.
- 蒙特卡洛模拟与网格搜索和k-fold交叉验证用于超参数调整.
主要成果:
- 开发的算法准确量化了斑点位移.
- 基于CNN的方法有效优化了关键的重叠参数.
- 该方法证明了检测多个转换运动的能力.
- 使用模拟和真实斑点模式的验证是成功的.
结论:
- 拟议的方法为斑点位移分析提供了一种精确而通用的方法.
- 整合CNN显著提高了算法的准确性和稳定性.
- 这种技术在各种应用中具有先进的质量控制和变形分析的前景.
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