Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Abstract

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.

32pages, 20 figures, 12 tables. A literature review paper. Journal

References in corpus (12)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• YOLOv4: Optimal Speed and Accuracy of Object Detection
• Deep Learning for Chest X-ray Analysis: A Survey
• Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net
• Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images
• Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans
• Classification of diffraction patterns in single particle imaging experiments performed at X-ray free-electron lasers using a convolutional neural network
• Machine-learning based methodologies for 3d x-ray measurement, characterization and optimization for buried structures in advanced ic packages
• Reference Setup for Quantitative Comparison of Segmentation Techniques for Short Glass Fiber CT Data
• Instance Segmentation of Fibers from Low Resolution CT Scans via 3D Deep Embedding Learning
• Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans
• Recognition of Defective Mineral Wool Using Pruned ResNet Models

Cited by in corpus (4)

• Visual inspection for illicit items in X-ray images using Deep Learning
• Augmentation Matters: A Mix-Paste Method for X-Ray Prohibited Item Detection under Noisy Annotations
• Illicit object detection in X-ray images using Vision Transformers
• Recognition of Defective Mineral Wool Using Pruned ResNet Models