Exploring the Frontiers of Synthetic Image-Based Deep Learning Training in Digital X-ray Radiography

Abstract

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Nevertheless, the conventional human-based approaches to carrying out industrial radiography are prone to challenges that negatively impact the accuracy and efficiency of defect detection. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these ADR algorithms demand large volumes of qualitative data that should be representative of real-world cases to be expected during deployment. However, the availability of digital X-ray radiography data, especially for open research, is limited by non-disclosure contractual terms in the industry. In this study, we present a pipeline that is capable of modelling synthetic images based on real digital X-ray radiography images. This is achieved through a systematic analysis of the intensity distribution, considering grey value (GV) statistical uniqueness related to exposure conditions used during image acquisition, type of imaged component, material thickness variations, X-ray beam divergence, anode heel effect, scatter radiation, edge delineation, etc. The generated synthetic images were exclusively utilized to train a deep learning model, yielding an impressive model performance with mean intersection over union (IoU) of 0.93, and mean dice coefficient of 0.96 when tested on real unseen digital X-ray radiography images. The presented methodology is scalable and adaptable, making it suitable for diverse industrial applications.