Abstract

The ferrite magnetic tiles are widely used in industry field. At present, the defects detection in ferrite magnetic tile surfaces is done by manual work. In order to improve the defects detection efficiency and prevent missed and false detection, an automatic detection system applied to the magnetic tiles non-destructive detection was proposed based on computer vision. A suit of the automatic defects detection equipment used for magnetic tile surfaces was designed so that we can adjust the position and angle of the lightings and cameras programmatically to meet the requirement of different location for different kinds of magnetic tiles. To solve the problem of automatically detect defects from magnetic tile images which are with dark colors and low contrasts, a new hybrid algorithm which combines nonsubsampled Contourlet transform and Laws texture feature measurement was proposed to eliminate the influence of the grinding textures and extract defects. In this methodology the original image was first decomposed by nonsubsampled Contourlet transform, the characteristics of the decomposition coefficients are analyzed by Laws texture feature measurement. Then according to the texture energies of the restructured image, a segmentation threshold was determined to reset the decomposition coefficients. Finally the image was reconstructed with the reconstruction coefficients, the grinding textures were eliminated and defects were obtained by Canny operator. The experimental results show that based on the proposed method, the grinding textures can be eliminated effectively, the defects can be extracted accurately, and the accuracy rate of extraction defects can achieve 93.57%. The automatic detection system can provide an effective solution to magnetic tile defects detection industry.

中文翻译：

---

基于非下采样Contourlet变换和纹理特征测量的铁氧体磁砖缺陷检测

摘要

铁氧体磁砖广泛用于工业领域。目前，铁氧体磁砖表面的缺陷检测是通过手工完成的。为了提高缺陷检测效率，防止漏检和误检，提出了一种基于计算机视觉的磁砖无损检测自动检测系统。设计了一套用于磁砖表面的自动缺陷检测设备，以便我们可以通过编程方式调整照明设备和摄像头的位置和角度，以满足不同种类磁砖的不同位置的要求。为了解决自动检测深色和低对比度的磁贴图像中的缺陷的问题，提出了一种结合非下采样Contourlet变换和Laws纹理特征测量的混合算法，以消除磨削纹理的影响并提取缺陷。在这种方法中，首先通过非下采样的Contourlet变换分解原始图像，然后通过Laws纹理特征测量分析分解系数的特征。然后根据重构图像的纹理能量，确定分割阈值以重置分解系数。最后，利用重建系数对图像进行重建，消除磨削纹理，并由Canny算子获得缺陷。实验结果表明，基于该方法，可以有效消除磨削织构，可以准确地提取缺陷，提取缺陷的准确率达到93.57％。自动检测系统可以为磁砖缺陷检测行业提供有效的解决方案。