University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia.
INETEC Institute for Nuclear Technology, Zagreb, Croatia.
Ultrasonics. 2022 Feb;119:106610. doi: 10.1016/j.ultras.2021.106610. Epub 2021 Oct 27.
Ultrasonic imaging is widely used for non-destructive evaluation in various industry applications. Early detection of defects in materials is the key to keeping the integrity of inspected structures. Currently, there have been some attempts to develop models for automated defect detection on ultrasonic data. To push the performance of these models even further more data is needed to train deep convolutional neural networks. A lot of data is also needed for training human experts. However, gathering a sufficient amount of data for training is a challenge due to the rare occurrence of defects in real inspection scenarios. This is why inspection results heavily depend on the inspector's previous experience. To overcome these challenges, we propose the use of Generative Adversarial Networks for generating realistic ultrasonic images. To the best of our knowledge, this work is the first one to show that a Generative Adversarial Network is able to generate images indistinguishable from real ultrasonic images. The most thorough statistical quality analysis to date of generated ultrasonic images has been conducted with the participation of human expert inspectors. The experimental results show that images generated using our Generative Adversarial Network provide the highest quality images compared to other published methods.
超声成像是在各种工业应用中进行无损评估的常用方法。早期发现材料缺陷是保持被检测结构完整性的关键。目前，已经有一些尝试开发用于超声数据自动缺陷检测的模型。为了进一步提高这些模型的性能，需要更多的数据来训练深度卷积神经网络。训练人类专家也需要大量的数据。然而，由于在实际检测场景中缺陷很少发生，因此收集足够数量的数据进行训练是一个挑战。这就是为什么检测结果严重依赖于检测员的先前经验。为了克服这些挑战，我们提出使用生成对抗网络来生成逼真的超声图像。据我们所知，这项工作首次表明生成对抗网络能够生成与真实超声图像无法区分的图像。最彻底的生成超声图像的统计质量分析迄今为止已经有人类专家检测员参与。实验结果表明，与其他已发表的方法相比，使用我们的生成对抗网络生成的图像提供了最高质量的图像。
