论文标题
工业生产和安全应用中X射线数据的计算机愿景：一项全面调查
Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey
论文作者
论文摘要
X射线成像技术已在临床任务中使用数十年来揭示不同器官的内部状况，近年来，在行业，安全和地理位置等其他领域，它在其他领域变得越来越普遍。最近在X射线图像分析中使用了计算机视觉和机器学习技术的最新开发，使其更容易自动处理X射线图像和几个基于机器学习的对象（异常）检测，分类和分割方法。由于相关图像处理应用中深度学习的潜力很高，因此在大多数研究中都使用了它。这项调查回顾了有关使用计算机视觉和机器学习在工业生产和安全应用中进行X射线分析的最新研究，并涵盖了公开可用数据集中这些技术的应用，技术，评估指标，数据集和性能比较。我们还重点介绍了已发表的研究中的一些缺点，并为基于计算机视觉的X射线分析的未来研究提供了建议。
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.
