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首页> 外文期刊>Journal of fiber bioengineering and informatics >The High-speed Fabric Defect Detection Algorithm Based on the Image Layered Model
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The High-speed Fabric Defect Detection Algorithm Based on the Image Layered Model

机译：基于图像分层模型的高速织物疵点检测算法

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摘要

The high-speed fabric defect detection algorithm based on fabric image layered model is proposed to achieve the goal of accurate defect detection in the fabric production process. The image layered model assumes that fabric image is a superposition of the periodic texture background image, noise image, and defect image. Thus fabrics can be divided and conquered. Firstly, image preprocessing and mean sampling algorithms were used to suppress the background texture and interference image layer, and variances sampling was used for enhancing defect image layer. Secondly, the Otsu method was applied for determining the segmentation threshold to segment the defect image automatically, then clear and accurate defect image was obtained via image post-treatment algorithm. Finally, defect positions were marked by a labeling algorithm to prepare for subsequent o2ine processing. Experiments on common defect images from a standard defect library were described, and experimental results show that the proposed algorithm based on image layered model is reliable, accurate, real-time and well used in the industrial field.
机译：提出了一种基于织物图像分层模型的高速织物缺陷检测算法，以达到在织物生产过程中准确检测缺陷的目的。图像分层模型假定织物图像是周期性纹理背景图像，噪声图像和缺陷图像的叠加。因此，织物可以被分开和征服。首先，使用图像预处理和均值采样算法来抑制背景纹理和干涉图像层，并使用方差采样来增强缺陷图像层。其次，采用Otsu方法确定分割阈值，对缺陷图像进行自动分割，然后通过图像后处理算法获得清晰准确的缺陷图像。最后，通过标记算法标记缺陷位置，以准备后续的氧气处理。对标准缺陷库中常见缺陷图像进行了实验，实验结果表明，所提出的基于图像分层模型的算法可靠，准确，实时，在工业领域得到了很好的应用。

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