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首页> 外文期刊>Automation Science and Engineering, IEEE Transactions on >Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning
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Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning

机译:基于Fisher准则的深度学习可变形图案化织物缺陷检测

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摘要

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.
机译:在本文中,当只有有限的负样本可用时,我们提出了用于图案化织物缺陷检测的判别表示。通过基于Fisher准则的堆叠式降噪自动编码器(FCSDA),可以将织物补丁有效地分为无缺陷和有缺陷类别。首先,将织物图像划分为相同大小的小块,然后将有缺陷和无缺陷的样本用于训练FCSDA。其次,通过FCSDA将测试补丁分类为有缺陷和无缺陷类别。最后,计算重建图像和缺陷补丁之间的残差,并通过阈值确定缺陷位置。实验结果证明了该方案在周期性花纹织物和较复杂的提花经编织物的缺陷检测中的有效性。

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