Karimi Mohammad H, Asemani Davud
Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering Faculty, K.N. Toosi University of Technology, Shariati Avenue, Tehran 1355-16315, Iran.
Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering Faculty, K.N. Toosi University of Technology, Shariati Avenue, Tehran 1355-16315, Iran.
ISA Trans. 2014 May;53(3):834-44. doi: 10.1016/j.isatra.2013.11.015. Epub 2014 Feb 4.
Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated.
陶瓷和瓷砖行业必须包含一个分级阶段，以量化产品质量。实际上，人工控制系统常用于分级目的。自动分级系统对于加强产品的质量控制和市场营销至关重要。由于瓷砖生产线的各个阶段通常会出现六种不同类型的缺陷，其纹理和形态各异，因此人们提出了许多图像处理技术用于缺陷检测。本文对用于检测表面缺陷的模式识别和图像处理算法进行了综述。每种方法在检测某些缺陷子组时似乎都有局限性。检测技术可分为三大类：统计模式识别、特征向量提取和纹理/图像分类。小波变换、滤波、形态学和轮廓波变换等方法在预处理任务中更有效。其他方法包括统计方法、神经网络和基于模型的算法，可用于提取表面缺陷。虽然，统计方法通常适用于识别诸如斑点等大缺陷，但小波处理等技术对于检测诸如针孔等小缺陷能提供可接受的响应。本文对每个子组中的现有算法进行了全面综述。此外，还讨论了评估参数，包括监督和非监督参数。使用各种性能参数，对不同的缺陷检测算法进行了比较和评估。
