针对周期性纹理背景影响织物缺陷检测效果的问题,提出了一种基于粗糙度测量和颜色距离的织物缺陷检测方法。该方法先将待检测图像由RGB颜色空间转换到HSV颜色空间,并分别对三通道进行同态滤波处理,以提升缺陷与背景之间的对比度;利用粗糙度测量对织物图像进行分类,并将同一类别的织物图像分成大小相同且互不重叠的图像分块,分别估计各个图像分块与其八邻域图像分块的颜色距离,从而实现对缺陷的粗定位;最后对粗定位图像分块进行显著性和二值化处理,有效减少了周期性纹理背景对检测结果的影响。实验结果表明:与近期4种方法相比,本文方法对周期性纹理织物图像表现出了较好的检测效果,检测准确率更高。

Aiming at the problem that periodic texture background affects the fabric defect detection, a fabric defect detection method based on coarseness measurement and color distance is proposed. Firstly, the detected image is transformed from RGB color space to HSV color space, and homomorphic filtering is carried out for three channels respectively to improve the contrast between defect and background. Fabric images are classified by coarseness measurement, the same categories of fabric images are divided into the same size and non-overlapping image blocks, and the color distances of each image block and its eight-neighbor image blocks are estimated respectively, so as the implementation of the rough localization of the defects can be done. Finally, the saliency and binary processing are performed on the rough location image blocks, which can effectively reduce the influence of the periodic texture background on the detection results. The experimental results show that compared with four methods proposed recently, the proposed method shows a better detection effect on the periodic texture fabric image, and the detection accuracy is higher.