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

TL;DR

This paper introduces a GAN-based method with an object detector discriminator to generate synthetic ultrasonic B-scans with defects, improving defect detection accuracy through data augmentation.

Contribution

The paper presents a novel GAN model that generates defect-laden ultrasonic B-scans and demonstrates its effectiveness in enhancing deep learning-based defect detection.

Findings

Synthetic B-scans improve detection accuracy.

Mixing real and generated data yields best results.

Generated data can compensate for limited datasets.

Abstract

Non-destructive testing is a set of techniques for defect detection in materials. While the set of imaging techniques are manifold, ultrasonic imaging is the one used the most. The analysis is mainly performed by human inspectors manually analyzing recorded images. The low number of defects in real ultrasonic inspections and legal issues considering data from such inspections make it difficult to obtain proper results from automatic ultrasonic image (B-scan) analysis. In this paper, we present a novel deep learning Generative Adversarial Network model for generating 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 performance of deep convolutional neural object detection networks. Our novel method is demonstrated on a dataset of almost 4000 B-scans with more than 6000 annotated defects. Defect detection performance when training on real data yielded average precision of 71%. By training only on generated data the results increased to 72.1%, and by mixing generated and real data we achieve 75.7% average precision. We believe that synthetic data generation can generalize to other challenges with limited datasets and could be used for training human personnel.