Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans

Abstract

Non-destructive testing is a set of techniques for defect detection in materials. While the set of imaging techniques is manifold, ultrasonic imaging is the one used the most. The analysis is mainly performed by human inspectors manually analyzing the acquired images. A low number of defects in real ultrasonic inspections and legal issues concerning data from such inspections make it difficult to obtain proper results from automatic ultrasonic image (B-scan) analysis. The goal of presented research is to obtain an improvement of the detection results by expanding the training data set with realistic synthetic samples. In this paper, we present a novel deep learning Generative Adversarial Network model for generating realistic ultrasonic B-scans with defects in distinct locations. Furthermore, we show that generated B-scans can be used for synthetic data augmentation, and can improve the performances of deep convolutional neural object detection networks. Our novel method was developed on a dataset with almost 4000 images and more than 6000 annotated defects. When trained only on real data, detector can achieve an average precision of 70%. By training only on generated data the results increased to 72%, and by mixing generated and real data we achieve almost 76% average precision. We believe that synthetic data generation can generalize to other tasks with limited data. It could also be used for training human personnel.