量化X射线散射对实时缺陷检测数据生成的影响
Vladyslav Andriiashen1, Robert van Liere1,2, Tristan van Leeuwen1,3
1Computational Imaging, Centrum Wiskunde en Informatica, Amsterdam, The Netherlands.
Journal of X-ray science and technology
|May 3, 2024
概括
在生成的图像中模拟X射线散射显著影响缺陷检测的准确性,特别是对于较小的缺陷. 散射与原始的比率对于使用深度卷积神经网络的可靠工业检查至关重要.
科学领域:
- 工业非破坏性测试 工业非破坏性测试
- 医学成像物理 医学成像物理
- 机器学习用于质量控制.
背景情况:
- 对于工业缺陷检测,X射线成像至关重要,需要准确和快速的算法.
- 深度卷积神经网络 (DCNNs) 提供高性能,但需要大量的标记数据.
- 生成现实的X射线数据具有挑战性,需要模拟物理效应的方法.
研究的目的:
- 量化评估X射线散射对缺陷检测准确性的影响.
- 评估模拟散射如何影响工业检查中DCNN的性能.
- 为了确定散射与初级比对检测能力的影响.
主要方法:
- 使用蒙特卡洛模拟来生成X射线散射分布.
- 在数据集上训练DCNN,并没有模拟的散射效应.
- 使用检测概率 (POD) 曲线来比较性能,重点是最小的可检测缺陷大小.
主要成果:
- 没有散射训练的DCNN检测到的缺陷>1.3毫米;结合散射,性能提高了<5%.
- 在散射与初级比率高的病例中 (1 < SPR < 5),性能差异达到了15% (~0.4毫米).
- 排除散射的影响在最小的可检测缺陷中最为明显.
结论:
- 从训练数据中排除X射线散射显著影响了小缺陷的检测.
- 散射与初级比率是影响检测性能和数据生成准确性的关键因素.
- 精确的散射模拟对于基于DCNN的强大的工业X射线检查系统至关重要.
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