基于超声数据的无成像空洞检测深度学习：连接数值数据与模型实验
Deep learning for imaging-free voids detection by ultrasonic data: bridging numerical data and model experiments.
摘要(中文译文)
基于超声的无损检测（NDT）广泛用于内部缺陷检测。为提高缺陷检测效率，深度学习（DL）已被引入超声无损检测工作流程。然而，传统方法依赖基于计算机视觉的深度学习从成像后数据中识别缺陷，其性能与图像和超声数据的质量高度相关。因此，端到端策略更为合适，因其可直接从超声数据重建速度场，并通过速度差异检测缺陷。端到端方法在数值测试中已展现出优异性能，但在现场应用中缺乏预测结果与真实模型之间直接且精确的对比。为此，本研究提出了一种基于全卷积网络（FCN）的端到端、无需成像的内部空洞检测框架。该网络通过数值测试和模型实验进行了验证。本研究的方法包括全卷积网络、SH波正演模拟、稀疏道频率-波数（F-K）滤波和图像处理。稀疏道F-K滤波减轻了模型实验中的边界反射，图像处理实现了从有限超声数据到高分辨率速度模型的映射。全卷积网络在1，200个随机生成的空洞模型上进行训练，在数值测试中取得了中位数交并比（IoU）为0.70（最大值为0.92，最小值为0.11）。此外，该模型利用阵列超声装置在预设空洞的均质玻璃块上采集的实验数据进行了验证。全卷积网络成功地从实验超声数据中直接预测了内部速度模型，准确识别了空洞的空间分布。模型实验结果表明了从合成数据到物理测量的成功过渡，凸显了无需成像的全卷积网络在工程场景中进行多尺度空洞检测的潜力。
Abstract
Non-destructive testing (NDT) based on ultrasonics is widely used for internal defect detection. To enhance the efficiency of defect detection, Deep Learning (DL) has been incorporated into ultrasonic NDT workflows. However, conventional methods rely on computer-vision-based DL to identify defects from post-imaging data, and their performance is highly related to the quality of the image and ultrasonic data. Consequently, end-to-end strategies are more suitable, as they can reconstruct the velocity field directly from ultrasonic data and detect defects by the difference of velocities. End-to-end methods have demonstrated excellent performance in numerical tests. However, they lack direct and precise comparison between predictions and true models in field application. Thus, this study presented an end-to-end, imaging-free framework for inner void detection by a Fully Convolutional Network (FCN). This net was tested by numerical tests and model experiments. The methodology in this study includes FCN, SH-wave forward modeling, sparse traces frequency-wavenumber (F-K) filtering, and image processing. The sparse traces F-K filtering mitigates boundary reflections in model experiments, and image processing realizes the mapping from limited ultrasonic data to a high-resolution velocity model. The FCN was trained on 1,200 randomly generated void models, achieving a median Intersection over Union (IoU) of 0.70 (with a maximum of 0.92, a minimum of 0.11) in numerical tests. Furthermore, the model was validated using experimental data acquired via an array ultrasound device on a homogeneous glass block with preset voids. The FCN successfully predicted the internal velocity model directly from the experimental ultrasonic data, accurately identifying the spatial distribution of the voids. The model experiment results demonstrate a successful transition from synthetic data to physical measurement, highlighting the potential of imaging-free FCN for multi-scale void detection in engineering scenarios.
如何引用
AMA
Shujie Chen, Zhenming Shi, Liu Liu, Ming Peng, Qiyu Wu. Deep learning for imaging-free voids detection by ultrasonic data: bridging numerical data and model experiments.. Ultrasonics. 2027; doi:10.1016/j.ultras.2026.108268.
APA
Shujie Chen, Zhenming Shi, Liu Liu, Ming Peng, Qiyu Wu (2027). Deep learning for imaging-free voids detection by ultrasonic data: bridging numerical data and model experiments.. Ultrasonics. https://doi.org/10.1016/j.ultras.2026.108268
GB/T 7714
Shujie Chen, Zhenming Shi, Liu Liu, Ming Peng, Qiyu Wu. Deep learning for imaging-free voids detection by ultrasonic data: bridging numerical data and model experiments.[J]. Ultrasonics, 2027 doi:10.1016/j.ultras.2026.108268.
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