Proceedings ArticleDOI
Automatic Defect Recognition in X-Ray Testing Using Computer Vision
Domingo Mery,Carlos Arteta +1 more
- pp 1026-1035
TLDR
A new dataset containing around 47.500 cropped X-ray images of 32 32 pixels with defects and no-defects in automotive components is released and 24 computer vision techniques including deep learning, sparse representations, local descriptors and texture features are evaluated and compared.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 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.read more
Citations
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SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images
TL;DR: A large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated, and proposes an approach named class-balanced hierarchical refinement (CHR) to deal with these difficulties.
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Automatic localization of casting defects with convolutional neural networks
TL;DR: This work examines how several different CNN architectures can be used to localize casting defects in X-ray images and takes advantage of transfer learning to allow state-of-the-art CNN localization models to be trained on a relatively small dataset.
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Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning
TL;DR: Research indicated that the three proposed improvement approaches performed better than baseline Faster R-CNN in X-ray image defect detection of automobile aluminum casting parts.
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Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network
TL;DR: A novel complementary attention network is designed by connecting the novel channel-wise attention subnetwork with spatial attention sub network sequentially, which adaptively suppresses the background noise features and highlights the defect features simultaneously by employing the complementary advantage of the channel features and spatial position features.
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Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging
Samet Akcay,Toby P. Breckon +1 more
TL;DR: This paper aims to review computerised X-ray security imaging algorithms by taxonomising the field into conventional machine learning and contemporary deep learning applications, with a particular focus on object classification, detection, segmentation and anomaly detection tasks.
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