X-Ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case Study

Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen, K. Joost Batenburg

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

X-ray imaging can be efficiently used for high-throughput in-line inspection of industrial products. However, designing a system that satisfies industrial requirements and achieves high accuracy is a challenging problem. The effect of many system settings is application-specific and difficult to predict in advance. Consequently, the system is often configured using empirical rules and visual observations. The performance of the resulting system is characterized by extensive experimental testing. We propose to use computational methods to substitute real measurements with generated images corresponding to the same experimental settings. With this approach, it is possible to observe the influence of experimental settings on a large amount of data and to make a prediction of the system performance faster than with conventional methods. We argue that a high accuracy of the image generator may be unnecessary for an accurate performance prediction. We propose a quantitative methodology to characterize the quality of the generation model using Probability of Detection curves. The proposed approach can be adapted to various applications and we demonstrate it on the poultry inspection problem. We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system.
Original languageEnglish
Article number79
Number of pages13
JournalJournal of Nondestructive Evaluation
Volume43
Issue number3
DOIs
Publication statusPublished - Sept 2024

Keywords

  • Deep Convolutional neural networks
  • Dual-energy X-ray
  • Probability of detection
  • System Design
  • X-ray inspection

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