Development of Digital NDT Methodology: Data Augmentation for Automated Fluorescent Penetrant Inspection of Aircraft Engine Blades

Fluorescent Penetrant Inspection (FPI) is a widely used inspection technique in the aerospace industry. Because of the aging aerospace sector, and because of the safety-criticality of the inspection, aerospace companies aim to automate (parts of this) inspection process to support inspectors. This paper focuses on a model that can assist inspectors by detecting (possible) defects. YOLOv8 is selected as the object detection model. For training such models, a dataset of sufficient size and variety is necessary to ensure good performance and to prevent overfitting. Because data acquisition is still in its beginning stages, an insufficient amount of data has been acquired. In this paper, we propose a data augmentation technique named Mosaic to artificially create more training data. This technique is tested by applying it to the acquired dataset numerous times and using the resulting dataset to train models with a static architecture (YOLOv8), after which the trained models are evaluated. The best trained model had a 0.834 mAP(50-95) performance, which is an increase of 0.666 mAP(50-95) over its baseline (the model trained on the dataset without data augmentation applied). The results show that, by using this Mosaic technique, promising object detection performance via Deep Convolutional Neural Networks (DCNNs) can be achieved even when the data are limited.