X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

翻译：几十年来,X射线成像技术被用于临床工作,以揭示不同器官的内部状况,近年来,这种技术在工业、安全和地理等其他领域越来越普遍,最近开发的计算机视觉和机器学习技术也使自动处理X射线图像和一些基于机器学习的物体(异常)探测、分类和分解方法在X射线图像分析中最近得以使用。由于在相关图像处理应用中深层学习的潜力很大,这种技术在大多数研究中都得到使用。这项调查审查了最近关于利用计算机视觉和机器学习进行工业生产和安全应用X射线分析的研究,并涵盖了在公开可得的数据集中应用、技术、评价指标、数据集和这些技术的性能比较。我们还着重指出了已出版的研究中的一些缺陷,并就今后基于计算机的X射线分析研究提出建议。