Paper Info
Title
Automatic Defect Recognition In X-Ray Testing Using Computer Vision
Abstract
To ensure safety in the construction of important metallic components for roadworthiness, it is necessary to check every component thoroughly using non-destructive testing. In last decades, X-ray testing has been adopted as the principal non-destructive testing method to identify defects within a component which are undetectable to the naked eye. Nowadays, modern computer vision techniques, such as deep learning and sparse representations, are opening new avenues in automatic object recognition in optical images. These techniques have been broadly used in object and texture recognition by the computer vision community with promising results in optical images. However, a comprehensive evaluation in X-ray testing is required. In this paper, we release a new dataset containing around 47.500 cropped X-ray images of 32 x 32 pixels with defects and no-defects in automotive components. Using this dataset, we evaluate and compare 24 computer vision techniques including deep learning, sparse representations, local descriptors and texture features, among others. We show in our experiments that the best performance was achieved by a simple LBP descriptor with a SVM-linear classifier obtaining 97% precision and 94% recall. We believe that the methodology presented could be used in similar projects that have to deal with automated detection of defects.
Year
DOI
Venue
2017
10.1109/WACV.2017.119
2017 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2017)
Field
DocType
ISSN
Automatic object recognition,Computer vision,Pattern recognition,Computer science,Local binary patterns,Feature extraction,Artificial intelligence,Pixel,Solid modeling,Deep learning,Classifier (linguistics),Automotive industry
Conference
2472-6737
Citations 
PageRank 
References 
5
0.47
20
Authors
2
Name
Order
Citations
PageRank
Domingo Mery146642.09
Carlos Arteta21448.15