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Survey of automated fabric inspection in textile industries

机译：纺织行业自动面料检查调查

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Defect review of fabric is also a method that accomplished with human visual scrutiny victimization semi machine-driven approach. To cut back time and value wastage because of defects the machine-driven review system for defect detection is employed for this purpose. The investment in machine-driven material defect detection is over economical once reduction parturient value and associated benefits square measure thought of the event of altogether machine-driven net examination system desires sturdy and economical material defect detection algorithms. The examination of real material defects is especially difficult thanks to the big vary of material defect categories that square measure characterized by their incomprehensibility and numerous techniques square measure developed to sight material defects and additionally the aim of this paper is to reason and or describe these algorithms. This paper makes a shot to gift the first survey on material defect detection techniques machine-driven and computer vision examination. Categorization of material detection of defect techniques is beneficial in evaluating the fabric qualities of well-known options. The characterization of real material surfaces victimization their structure and the primitive set have not nonetheless been prosperous. Automatic material examination victimization machine vision with pattern matching algorithmic rule can scale back the time for examination and human error than alternative defect identification and classification algorithmic.
机译：织物的缺陷检查也是一种通过人类视觉检查受害半机器驱动的方法来完成的方法。为了减少由于缺陷而造成的时间浪费和价值浪费，为此采用了机器驱动的缺陷检测系统。一旦降低了机器的产值和相关的效益平方测算，机器驱动的材料缺陷检测的投资就完全是机器驱动的网络检查系统所需要的坚固而经济的材料缺陷检测算法。实际材料缺陷的检查尤为困难，这是由于材料缺陷类别的差异很大，其平方特征以其不可理解性为特征，并且开发了多种方法来发现材料缺陷，并且本文的目的是推理和描述这些算法。本文旨在赠与有关材料缺陷检测技术（机器驱动和计算机视觉检查）的首次调查。对缺陷技术进行材料检测的分类有助于评估众所周知的选件的织物质量。尽管如此，真实材料表面的表征却损害了它们的结构和原始集，但并没有繁荣起来。与模式缺陷识别和分类算法相比，具有模式匹配算法规则的自动材料检查受害机器视觉可以减少检查时间和人为错误。

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