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首页> 外文期刊>Fibres & textiles in Eastern Europe >Fabric Defect Detection Using the Sensitive Plant Segmentation Algorithm
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Fabric Defect Detection Using the Sensitive Plant Segmentation Algorithm

机译：织物缺陷检测使用敏感植物分割算法

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摘要

Fabric quality control and defect detection are playing a crucial role in the textile industry with the development of high customer demand in the fashion market. This work presents fabric defect detection using a sensitive plant segmentation algorithm (SPSA) which, is developed with the sensitive behaviour of the sensitive plant biologically named "mimosa pudica". This method consists of two stages: The first stage enhances the contrast of the defective fabric image and the second stage segments the fabric defects with the aid of the SPSA. The SPSA proposed was developed for defective pixel identification in non-uniform patterns of fabrics. In this paper, the SPSA was built through checking with devised conditions, correlation and error probability. Every pixel was checked with the algorithm developed to be marked either a defective or non-defective pixel. The SPSA proposed was tested on different types of fabric defect databases, showing a much improved performance over existing methods.
机译：纺织品质量控制和缺陷检测在纺织业中发挥了至关重要的作用，随着时装市场的高客户需求的发展。该工作采用敏感植物分割算法（SPSA）提供了织物缺陷检测，该算法是通过生物学名为“Mimosa Pudica”的敏感植物的敏感行为而开发的。该方法由两个阶段组成：第一阶段增强了缺陷织物图像的对比度和第二级段借助于SPSA的织物缺陷。建议的SPSA用于以非均匀的织物图案的缺陷像素识别。在本文中，SPSA是通过检查条件，相关性和误差概率检查的。使用开发的算法检查每个像素，以标记有缺陷或非缺陷像素。建议的SPSA在不同类型的织物缺陷数据库上进行测试，显示出对现有方法的更大提高性能。

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