Journal Article10.1016/J.NDTEINT.2013.10.006
Detection of surface crack defects on ferrite magnetic tile
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TL;DR: In this paper, a new approach is proposed for automatically detecting crack defects with dark colors and low contrasts in magnetic tile images using the fast discrete curvelet transform (FDCT) and texture analysis.
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Abstract: A new approach is proposed for automatically detecting crack defects with dark colors and low contrasts in magnetic tile images using the fast discrete curvelet transform (FDCT) and texture analysis. In this methodology the original images were first decomposed and reconstructed based on the FDCT. Then the thresholds of decomposition coefficients were calculated by texture feature measurements. With these thresholds the surface textures in the images can be eliminated. Finally by extracting contours from the reconstructed images, the expected images without textures but with crack defects contours were obtained. Experimental results show that the proposed method could eliminate the contours of the textures, and extract from the image cracks longer than 0.8 mm.
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Citations
Crack detection using image processing: A critical review and analysis
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TL;DR: In this paper, a detailed survey is conducted to identify the research challenges and the achievements till in this field, and those research papers are reviewed based on the image processing techniques, objectives, accuracy level, error level, and the image data sets.
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TL;DR: It is shown that the model exceeds the state of the art in saliency detection of magnetic tiles, in which it both effectively and explicitly maps multiple surface defects from low-contrast images.
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Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY.
Tamas Czimmermann,Gastone Ciuti,Mario Milazzo,Marcello Chiurazzi,Stefano Roccella,Calogero Maria Oddo,Paolo Dario +6 more
TL;DR: This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles, and describes artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way.
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Automated defect inspection system for metal surfaces based on deep learning and data augmentation
TL;DR: A new convolutional variational autoencoder (CVAE) and deep CNN-based defect classification algorithm to solve the problem of automatic defect inspection in the metal manufacturing industry.
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Surface Defect Detection via Entity Sparsity Pursuit With Intrinsic Priors
TL;DR: This paper proposes an entity sparsity pursuit (ESP) method to identify surface defects that is compact and able to detect surface defects in an unsupervised manner, and demonstrates that ESP outperforms state-of-the-art methods.
119
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