mlsanityData to 7 Oct 2026

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

arXiv 2211.05565 ↗First posted 10 Nov 2022cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 47 of its 177 references that are in the map. First posted on arXiv 10 Nov 2022.

Builds on

47 of 177 references.

References · 47

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2017Mask R-CNNcited by 7,259
  5. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  6. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  7. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  8. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  9. 2015Fast R-CNNcited by 2,930
  10. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  11. 2018YOLOv3: An Incremental Improvementcited by 2,652
  12. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
35 more
  1. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  2. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  3. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  4. 2018Path Aggregation Network for Instance Segmentationcited by 796
  5. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 539
  6. 2018Object Detection with Deep Learning: A Reviewcited by 524
  7. 2019Res2Net: A New Multi-scale Backbone Architecturecited by 468
  8. 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 428
  9. 2019Video Instance Segmentationcited by 391
  10. 2015Adversarial Autoencoderscited by 359
  11. 2016Top-down Neural Attention by Excitation Backpropcited by 318
  12. 2020Exploring Self-attention for Image Recognitioncited by 313
  13. 2017Understanding Convolution for Semantic Segmentationcited by 168
  14. 2018Gradient Harmonized Single-stage Detectorcited by 114
  15. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  16. 2020The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classificationcited by 64
  17. 2019SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Imagescited by 52
  18. 2020Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Modulecited by 39
  19. 2021Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmarkcited by 31
  20. 2021Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detectioncited by 30
  21. 2020Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imagingcited by 25
  22. 2018Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learningcited by 16
  23. 2019Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagerycited by 11
  24. 2020Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scanscited by 10
  25. 2018Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clusteringcited by 6
  26. 2019The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composited X-ray Imagerycited by 5
  27. 2019On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagerycited by 5
  28. 2021Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Netcited by 4
  29. 2021Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Imagescited by 4
  30. 2022Recognition of Defective Mineral Wool Using Pruned ResNet Modelscited by 3
  31. 2019Instance Segmentation of Fibers from Low Resolution CT Scans via 3D Deep Embedding Learningcited by 1
  32. 2019Reference Setup for Quantitative Comparison of Segmentation Techniques for Short Glass Fiber CT Datacited by 1
  33. 2019Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scanscited by 1
  34. 2020Classification of diffraction patterns in single particle imaging experiments performed at X-ray free-electron lasers using a convolutional neural networkcited by 1
  35. 2021Machine-learning based methodologies for 3d x-ray measurement, characterization and optimization for buried structures in advanced ic packagescited by 1

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 1.

6 papers.

  1. 2023On Pixel-level Performance Assessment in Anomaly Detectioncited by 6
  2. 2023Visual inspection for illicit items in X-ray images using Deep Learningcited by 3
  3. 2024Illicit object detection in X-ray images using Vision Transformerscited by 3
  4. 2024Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagerycited by 2
  5. 2024Fan-Beam CT Reconstruction for Unaligned Sparse-View X-ray Baggage Datasetcited by 0
  6. 2025X-ray illicit object detection using hybrid CNN-transformer neural network architecturescited by 0