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Fabric defect detection based on saliency histogram features

机译：基于显着直方图特征的织物疵点检测

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摘要

In order to increase the automatic quality control level in the textile industry, depending on the big data collected by the Internet of things of the textile factories, this paper proposes a novel visual saliency-based defect detection algorithm, which has the capability of automatically detecting defect in both nonpatterned and patterned fabrics. The algorithm employs the histogram features extracted from the saliency maps to detect the fabric defects. The algorithm involves three main steps: (1) saliency map generation to highlight the defective regions and suppress the defect-free regions, (2) saliency histogram features extraction and selection to obtain the feature vectors that can effectively discriminate between the defective and defect-free fabric images, and (3) fabric defect detection using a two-class support vector machine classifier that has been trained using sets of feature vectors extracted from defective and defect-free fabric samples. Experimental results show that our method yields accurate detections, outperforming other state-of-the-art algorithms.
机译：为了提高纺织行业的自动质量控制水平，根据纺织工厂物联网收集的大数据，提出了一种基于视觉显着性的新型缺陷检测算法，该算法具有自动检测能力。无图案和有图案的织物中的缺陷。该算法采用从显着性图提取的直方图特征来检测织物缺陷。该算法包括三个主要步骤：（1）显着性图生成以突出缺陷区域并抑制无缺陷区域；（2）显着性直方图特征提取和选择以获得可以有效地区分缺陷和缺陷的特征向量。免费的织物图像，以及（3）使用两类支持向量机分类器的织物缺陷检测，该分类器已使用从缺陷和无缺陷的织物样本中提取的特征向量集进行了训练。实验结果表明，我们的方法可产生准确的检测结果，优于其他最新算法。

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