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.

超声波成像广泛用于各种行业应用中的无损评估。及早发现材料中的缺陷是保持受检结构完整性的关键。目前，已经有一些尝试开发用于超声波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。培训人类专家也需要大量数据。然而，由于在实际检查场景中很少发生缺陷，因此收集足够数量的训练数据是一项挑战。这就是为什么检查结果在很大程度上取决于检查员以前的经验的原因。为了克服这些挑战，我们建议使用生成对抗网络来生成逼真的超声波图像。据我们所知，这项工作是第一个表明生成对抗网络能够生成与真实超声波图像无法区分的图像的工作。迄今为止，在人类专家检查员的参与下，对生成的超声波图像进行了最彻底的统计质量分析。实验结果表明，与其他已发布的方法相比，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。