Abstract:
In this paper, we propose a discriminative representation for patterned fabric defect detection when only limited negative samples are available. Fabric patches are effic...Show MoreMetadata
Abstract:
In this paper, we propose a discriminative representation for patterned fabric defect detection when only limited negative samples are available. Fabric patches are efficiently classified into defectless and defective categories by Fisher criterion-based stacked denoising autoencoders (FCSDA). First, fabric images are divided into patches of the same size, and both defective and defectless samples are utilized to train FCSDA. Second, test patches are classified through FCSDA into defective and defectless categories. Finally, the residual between the reconstructed image and defective patch is calculated, and the defect is located by thresholding. Experimental results demonstrate the effectiveness of the proposed scheme in the defect detection for periodic patterned fabric and more complex jacquard warp-knitted fabric.
Published in: IEEE Transactions on Automation Science and Engineering ( Volume: 14, Issue: 2, April 2017)
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- IEEE Keywords
- Index Terms
- Deep Learning ,
- Fabric Defect ,
- Fabric Defect Detection ,
- Negative Samples ,
- Image Reconstruction ,
- Complex Materials ,
- Image Patches ,
- Fabric Image ,
- Fault Samples ,
- Inspection System ,
- Denoising Autoencoder ,
- Fault Categories ,
- Automatic Inspection ,
- Neural Network ,
- Training Set ,
- Learning Rate ,
- Deep Network ,
- Scaling Factor ,
- False Positive Rate ,
- Detection Accuracy ,
- Plain Fabrics ,
- Hidden Layer ,
- Gabor Filters ,
- Softmax Classifier ,
- Spectral Method ,
- Network Depth ,
- Neurons In Layer ,
- Fault Location ,
- Wavelet Transform ,
- Feature Space
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Deep Learning ,
- Fabric Defect ,
- Fabric Defect Detection ,
- Negative Samples ,
- Image Reconstruction ,
- Complex Materials ,
- Image Patches ,
- Fabric Image ,
- Fault Samples ,
- Inspection System ,
- Denoising Autoencoder ,
- Fault Categories ,
- Automatic Inspection ,
- Neural Network ,
- Training Set ,
- Learning Rate ,
- Deep Network ,
- Scaling Factor ,
- False Positive Rate ,
- Detection Accuracy ,
- Plain Fabrics ,
- Hidden Layer ,
- Gabor Filters ,
- Softmax Classifier ,
- Spectral Method ,
- Network Depth ,
- Neurons In Layer ,
- Fault Location ,
- Wavelet Transform ,
- Feature Space
- Author Keywords