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首页> 外文期刊>Fibres & textiles in Eastern Europe >Fabric Defect Detection and Classifier via Multi-Scale Dictionary Learning and an Adaptive Differential Evolution Optimized Regularization Extreme Learning Machine
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Fabric Defect Detection and Classifier via Multi-Scale Dictionary Learning and an Adaptive Differential Evolution Optimized Regularization Extreme Learning Machine

机译：通过多尺度字典学习和自适应差分进化优化正则化极限学习机进行织物疵点检测和分类

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摘要

To develop an automatic detection and classifier model for fabric defects, a novel detection and classifier technique based on multi-scale dictionary learning and the adaptive differential evolution algorithm optimised regularisation extreme learning machine (ADE-RELM) is proposed. Firstly in onder to speed up dictionary updating under the condition of guaranteeing dictionary sparseness, k-means singular value decomposition (KSLD) dictionary learning is used. Then multi-scale KSVD dictionary learning is presented to extract texture features of textile images more accurately. Finally a unique ADE-RELM is designed to build a defect classifier model. In the training ADE-RELM classifier stage, a self-adaptive mutation operator is used to solve the parameter setting problem of the original differential evolution algorithm, then the adaptive differential evolution algorithm is utilised to calculate the optimal input weights and hidden bias of RELM. The method proposed is committed to detecting common defects like broken warp, broken weft, oil, and the declining warp of grey-level and pure colour fabrics. Experimental results show that compared with the traditional Gabor filter method, morphological operation and local binary pattern, the method proposed in this paper can locate defects precisely and achieve high detection efficiency.
机译：为了建立织物缺陷自动检测和分类器模型，提出了一种基于多尺度字典学习和自适应差分进化算法优化正则化极限学习机（ADE-RELM）的检测和分类器新技术。首先在保证字典稀疏性的前提下加快字典更新速度，采用k-均值奇异值分解（KSLD）字典学习方法。然后提出了多尺度KSVD字典学习算法，以更准确地提取纺织品图像的纹理特征。最后，设计了唯一的ADE-RELM来构建缺陷分类器模型。在训练ADE-RELM分类器阶段，使用自适应变异算子解决原始差分进化算法的参数设置问题，然后利用自适应差分进化算法计算RELM的最优输入权重和隐藏偏差。所提出的方法致力于检测常见的缺陷，例如断经，断纬，断油以及灰色和纯色织物的断经。实验结果表明，与传统的Gabor滤波方法，形态学运算和局部二值模式相比，本文提出的方法能够准确定位缺陷，达到较高的检测效率。

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