A tomographic workflow to enable deep learning for X-ray based foreign object detection

TL;DR

This paper introduces a CT-based workflow for generating training data for deep learning foreign object detection in X-ray images, reducing manual annotation effort and improving detection accuracy.

Contribution

It presents a novel method using CT scans of few representative objects to produce high-quality training data for deep learning, enhancing efficiency and accuracy.

Findings

Fewer than 10 CT-scanned objects suffice for effective detection.

The workflow yields higher detection accuracy than traditional radiograph annotation.

Object detection performance improves with increased CT reconstruction quality.

Abstract

Detection of unwanted (`foreign') objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labour requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that have been acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way compared to conventional radiograph annotation. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting. Moreover, for real experimental data we show that the workflow leads to higher foreign object detection accuracies than with standard radiograph annotation.