Spectral Analysis of Suface Waves Combined to Laser Ultrasonics for the Non-Destructive Testing of High Performance Materials and Composites

2007 ◽  
Author(s):  
E. Lafond ◽  
X. Zhang
Keyword(s):  
Spectral Analysis ◽  
High Performance ◽  
Laser Ultrasonics ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

Data augmentation by CycleGAN-based extra-supervised for non-destructive testing

2021 ◽  
Author(s):  
Jiangshan Ai ◽  
Lulu Tian ◽  
Libing Bai ◽  
Jie Zhang
Keyword(s):  
High Performance ◽  
Field Data ◽  
Large Scale ◽  
Data Augmentation ◽  
Learning Method ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Non Destructive

Abstract Deep learning method is widely used in computer vision tasks with large scale annotated datasets. However, it is a big challenge to obtain such datasets in most directions of the vision based non-destructive testing (NDT) field. Data augmentation is proved as an efficient way in dealing with the lack of large-scale annotated datasets. In this paper, we propose CycleGAN-based extra-supervised (CycleGAN-ES) to generate synthetic NDT images, where the ES is used to ensure that the bidirectional mapping are learned for corresponding label and defect. Furthermore, we show the effectiveness of using the synthesized images to train deep convolutional neural networks (DCNN) for defects recognition. In the experiments, we extract numbers of X-ray welding images with both defect and no-defect from the published GDXray dataset, CycleGAN-ES are used to generate the synthetic defect images based on a small number of extracted defect images and manually drawn labels which are used as a content guide. For quality verification of the synthesized defect images, we use a high-performance classifier pre-trained using big dataset to recognize the synthetic defects and show comparability of the performances of classifiers trained using synthetic defects and real defects respectively. To present the effectiveness of using the synthesized defects as an augmentation method, we train and evaluate the performances of DCNN for defects recognition with or without the synthesized defects.

A Real-Time Automatic Inspection System for Pattavia Pineapples

2006 ◽  
Vol 321-323 ◽  
pp. 1186-1191 ◽  
Author(s):  
Watcharin Kaewapichai ◽  
Pakorn Kaewtrakulpong ◽  
Asa Prateepasen
Keyword(s):  
Real Time ◽  
High Performance ◽  
Inspection System ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Recognition Phase ◽  
Histogram Intersection ◽  
Two Phases ◽  
Belt Transmission ◽  
Non Destructive

This paper presents a machine vision method to inspect the maturity of pineapples that ripe naturally. Unlike previous methods, the proposed technique can be categorized as a real-time non destructive testing (Real-Time NDT) approach. It consists of two phases, learning and recognition phases. In the learning phase, the system constructs a library of reference pineappleskin- color models. In the recognition phase, the same process is performed to build a pineappleskin- color model of the testing subject. The model is then compared with each of the reference in the library by a method called region-segmented histogram intersection. The subject is then labeled with the grade of the best match. The system achieved a high performance and speed (3 frames/sec.) in our experiment. The system also includes weighing machine on belt transmission for weight prediction.

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