基于数字图象处理的瓷砖表面缺陷检测的分析与评

基于数字图象处理的瓷砖表面缺陷检测的分析与评(中文5100字,英文8000字)
引言
    陶瓷、瓷砖行业必不可少地应该包括一种分级标准去评定产品的质量。实际上,人类的控制系统通常用于分级的目的。自动分级系统对于提高产品的质量控制和营销是至关重要的。因为来自瓷砖生产线的不同阶段与不同的材质和形态通常存在六种不同类型的缺陷,所以许多图像处理技术致力于缺陷检测研究。本文调查了已用于检测表面缺陷的模式识别和图像处理算法。每种方法似乎是有限的检测缺陷的一些小组。检测技术可以分为三类:统计模式识别、特征向量提取和纹理图像分类。方法如小波变换、滤波、形态学和轮廓变换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以应用于提取表面缺陷。尽管如此,统计方法通常适合识别大斑点等缺陷,而小波处理等技术为检测小针孔等缺陷提供一个可接受的方案。本文彻底调查了每个类别当前的算法。同时,评价的参数包括监督和非监督参数。对不同的缺陷检测算法在使用各种性能参数的情况下进行比较和评估。
a b s t r a c t
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.