Abstract—

A technique and an algorithm of digital surface image processing are proposed to increase the validity of real-time detection of small size defects. The algorithm is implemented in the MATLAB programming environment. The technique is based on segmentation of the high-frequency component of surface texture because small size defects are especially pronounced in this component. The high-frequency component, in particular roughness, is extracted by means of wavelet transform for frequency components separation and homomorphic filtration for compensation of low-frequency distortion caused by nonuniform illumination of test surface. Segmentation of the high-frequency texture component consists in formation of a binary image using the texture descriptors derived from the gray-level co-occurrence matrix as the segmentation threshold. The proposed technique and algorithm are approved in applications to defect detection for a simulated surface, for real ground surface of hardened steel, and for surfaces of carbon fiber reinforced plastic composite. Extraction efficiency of the high-frequency component of surface texture is shown. It is found that texture descriptors, “contrast’ and “energy,” can be applied as segmentation thresholds for defect extraction/determination on the ground (anisotropic) surface while segmentation of an image of a plastic composite (isotropic) surface is effective just with “energy” as a threshold. The proposed technique can be applied for simultaneously real-time monitoring the surface texture and detecting the small size defect in machine vision systems during production and operation of tribosystems.

中文翻译：

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技术表面缺陷实时检测技术

摘要-

提出了一种数字表面图像处理技术和算法，以提高小尺寸缺陷实时检测的有效性。该算法在MATLAB编程环境中实现。该技术基于表面纹理高频分量的分割，因为小尺寸缺陷在该分量中尤其明显。通过小波变换提取高频成分，特别是粗糙度成分，进行频率成分分离，并通过同态滤波来补偿由于测试表面光照不均匀引起的低频失真。高频纹理分量的分割包括使用从灰度共生矩阵导出的纹理描述符作为分割阈值来形成二值图像。所提出的技术和算法已被批准应用于模拟表面、硬化钢真实磨削表面以及碳纤维增强塑料复合材料表面的缺陷检测。显示了表面纹理高频分量的提取效率。研究发现，纹理描述符“对比度”和“能量”可以用作地面（各向异性）表面上的缺陷提取/确定的分割阈值，而塑料复合材料（各向同性）表面的图像分割只需使用以“能量”为门槛。该技术可用于在摩擦系统的生产和操作过程中同时实时监测表面纹理并检测机器视觉系统中的小尺寸缺陷。