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首页> 外文会议>International conference on graphic and image processing >Fabric Defect Detection Based on Visual Saliency Using Deep Feature and Low-rank Recovery
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Fabric Defect Detection Based on Visual Saliency Using Deep Feature and Low-rank Recovery

机译：基于视觉显着性的深度特征和低秩恢复的织物缺陷检测

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摘要

Fabric defect detection plays an important role in improving the quality of fabric product. In this paper, a novel fabric defect detection method based on visual saliency using deep feature and low-rank recovery was proposed. First, unsupervised training is carried out by the initial network parameters based on MNIST large datasets. The supervised fine-tuning of fabric image library based on Convolutional Neural Networks (CNNs) is implemented, and then more accurate deep neural network model is generated. Second, the fabric images are uniformly divided into the image block with the same size, then we extract their multi-layer deep features using the trained deep network. Thereafter, all the extracted features are concentrated into a feature matrix. Third, low-rank matrix recovery is adopted to divide the feature matrix into the low-rank matrix which indicates the background and the sparse matrix which indicates the salient defect. In the end, the iterative optimal threshold segmentation algorithm is utilized to segment the saliency maps generated by the sparse matrix to locate the fabric defect area. Experimental results demonstrate that the feature extracted by CNN is more suitable for characterizing the fabric texture than the traditional LBP, HOG and other hand-crafted features extraction method, and the proposed method can accurately detect the defect regions of various fabric defects, even for the image with complex texture.
机译：织物缺陷检测在提高织物产品质量中起着重要作用。提出了一种基于视觉显着性，深度特征和低秩恢复的织物缺陷检测新方法。首先，基于MNIST大型数据集的初始网络参数进行无监督训练。实现了基于卷积神经网络（CNN）的织物图像库的监督微调，然后生成了更精确的深度神经网络模型。其次，将织物图像均匀地划分为相同大小的图像块，然后使用经过训练的深度网络提取其多层深度特征。之后，将所有提取的特征集中到特征矩阵中。第三，采用低秩矩阵恢复将特征矩阵分为表示背景的低秩矩阵和表示显着缺陷的稀疏矩阵。最后，利用迭代最优阈值分割算法对稀疏矩阵生成的显着图进行分割，以定位出织物的缺陷区域。实验结果表明，与传统的LBP，HOG等手工特征提取方法相比，CNN提取的特征更适合表征织物的质地，并且该方法可以准确检测各种织物缺陷的缺陷区域，即使对于复杂纹理的图像。

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