Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationDefect DetectionGenerative Adversarial Networkobject-detectionObject DetectionSynthetic Data GenerationSimilar Papers 제목 키워드 기반
A Generative Approach for Detection-driven Underwater Image Enhancement
In this paper, we introduce a generative model for image enhancement specifically for improving diver detection in the underwater domain. In particular, we present a model that integrates generative adversarial network (…
Generative Adversarial NetworkImage EnhancementDeep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detection
Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make …
Deep Reinforcement LearningGenerative Adversarial Networkreinforcement-learningReinforcement Learning+1Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning
Anomaly detection has wide applications in machine intelligence but is still a difficult unsolved problem. Major challenges include the rarity of labeled anomalies and it is a class highly imbalanced problem. Traditional…
Active LearningAnomaly DetectionEnsemble LearningGenerative Adversarial Network+1Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called …
Generative Adversarial NetworkImage EnhancementObjectobject-detection+5Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification
In recent years, hyperspectral image (HSI) classification based on generative adversarial networks (GAN) has achieved great progress. GAN-based classification methods can mitigate the limited training sample dilemma to s…
ClassificationHyperspectral Image Classificationimage-classificationImage Classification