For any industry, an important part of quality control is the detection and identification of defects of the products. During the manufacturing process, a wide range of defects occur on or in the products. These defects have an impact on its esthetics and functionality of the end-product. Their ability to compete in the market and the manufacturing costs are to be compromised if they are not detected properly. In many industries, defect detection is done manually by a few trained inspectors. The inspection results of this kind are highly subjective, and their measurements are seldom repeatable. The availability of fast and specialized computing hardware nowadays has facilitated the emergence of image processing algorithms to do online and real time quality inspections. As a result, many industries, such as textiles, steel, semiconductors, wood, leather, etc., are looking forward to machine-vision based inspection systems to address the issues pertaining to manual inspection. A comprehensive literature review of defects Identification, Localization, and Classification methods proposed to implement in such imaging systems is presented in this paper. The advantages and disadvantages of these suggested and implemented these methods are compared. The performance metrics used for the evaluation of these methods are also presented. In addition, the utilization of Optimization Techniques and Artificial Neural network structures in these systems is investigated. This review can help the researcher engaged in the area of automatic texture inspection systems to investigate the texture identification, localization, and classification methods.

对于任何行业来说，质量控制的重要组成部分是产品缺陷的检测和识别。在制造过程中，产品表面或内部会出现各种各样的缺陷。这些缺陷会影响最终产品的美观和功能。如果检测不当，它们的市场竞争能力和制造成本都会受到影响。在许多行业中，缺陷检测是由一些经过培训的检查员手动完成的。此类检查结果具有很强的主观性，并且其测量结果很少可重复。如今快速且专业的计算硬件的出现促进了图像处理算法的出现，以进行在线和实时质量检查。因此，许多行业，如纺织、钢铁、半导体、木材、等，期待基于机器视觉的检测系统来解决与手动检测有关的问题。本文对建议在此类成像系统中实施的缺陷识别、定位和分类方法进行了全面的文献综述。比较了这些建议和实施的方法的优点和缺点。还介绍了用于评估这些方法的性能指标。此外，还研究了优化技术和人工神经网络结构在这些系统中的利用。本文的综述可以帮助从事自动纹理检测系统领域的研究人员研究纹理识别、定位和分类方法。