Automated defect detection by deep learning algorithms from tone burst eddy current thermography data

Quality control is of paramount importance in the modern industrial landscape. Infrared thermography (IRT) is a non-destructive testing (NDT) technique that in recent years has experienced notable advancements and found widespread applications in the domains of defect detection and material characterisation. Tone burst eddy current thermography (TBET) is an active IRT testing procedure that is based on the principle of electromagnetic induction. The method has already proven to be valuable due to its cost-effectiveness, fast inspection rates and non-invasive nature. These features make TBET an ideal tool for in-process evaluation of conductive materials and composites. With the emergence of object detection and segmentation algorithms, deep learning (DL) has been widely applied in the field of IRT. DL-empowered thermography can achieve automated real-time inspection of materials. Such systems align perfectly with the objective of zero-defect manufacturing by enabling active inspection and detection of defects. However, the lack of sufficient training data is one of the major hurdles to realising an intelligent quality control system. This work presents a methodology for bridging the data gap using synthetic TBET measurements generated from finite element method (FEM)-based simulations. The proposed approach utilises two object localisation deep neural networks: the faster region-based convolutional neural network (Faster R-CNN) with an Inception-v4 backbone and the You Only Look Once version 8 (YOLOv8) network, to enable interpretations from the synthetic thermal data for automated quality management. The results reveal the potential of adopting synthetically generated datasets for pre-training the DL algorithms and, henceforth, the effective deployment of an automated defect detection and quality monitoring system based on TBET.