National Metrology Institute of Japan (NMIJ), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, Tsukuba 305-8568, Japan.
Sensors (Basel). 2018 Nov 7;18(11):3820. doi: 10.3390/s18113820.
For many decades, ultrasonic imaging inspection has been adopted as a principal method to detect multiple defects, e.g., void and corrosion. However, the data interpretation relies on an inspector's subjective judgment, thus making the results vulnerable to human error. Nowadays, advanced computer vision techniques reveal new perspectives on the high-level visual understanding of universal tasks. This research aims to develop an efficient automatic ultrasonic image analysis system for nondestructive testing (NDT) using the latest visual information processing technique. To this end, we first established an ultrasonic inspection image dataset containing 6849 ultrasonic scan images with full defect/no-defect annotations. Using the dataset, we performed a comprehensive experimental comparison of various computer vision techniques, including both conventional methods using hand-crafted visual features and the most recent convolutional neural networks (CNN) which generate multiple-layer stacking for representation learning. In the computer vision community, the two groups are referred to as shallow and deep learning, respectively. Experimental results make it clear that the deep learning-enabled system outperformed conventional (shallow) learning schemes by a large margin. We believe this benchmarking could be used as a reference for similar research dealing with automatic defect detection in ultrasonic imaging inspection.
几十年来，超声成像检测一直被用作检测多种缺陷（如空洞和腐蚀）的主要方法。然而，数据解释依赖于检验员的主观判断，因此结果容易出现人为错误。如今，先进的计算机视觉技术为通用任务的高级视觉理解开辟了新的视角。本研究旨在开发一种使用最新视觉信息处理技术的高效自动超声图像分析系统，用于无损检测 (NDT)。为此，我们首先建立了一个包含 6849 张带有完整缺陷/无缺陷注释的超声扫描图像的超声检查图像数据集。使用该数据集，我们对各种计算机视觉技术进行了全面的实验比较，包括使用手工制作的视觉特征的传统方法和最近的卷积神经网络 (CNN)，后者用于表示学习的多层堆叠。在计算机视觉领域，这两组分别称为浅层学习和深度学习。实验结果清楚地表明，深度学习支持的系统大大优于传统（浅层）学习方案。我们相信，这种基准测试可以作为类似研究中超声成像检测中自动缺陷检测的参考。
