基于改进的加权中值滤波与K-means聚类的织物缺陷检测

为检测纹理织物在生产过程中产生的各种疵点,提出一种基于改进的加权中值滤波与K-means聚类相结合的纹理织物疵点检测方法。首先利用改进的加权中值滤波对纹理织物图像进行预处理,以减少纹理信息对疵点检测产生的影响,同时通过联合直方图动态数据分配权重和像素,减少寻求中位数的时间来有效地缩短检测时间,提高了执行速度;然后采用K-means算法对滤波后的织物图像进行聚类,计算织物图像疵点和非疵点的聚类中心,进而实现图像疵点区域的分割。实验结果表明,该方法可有效地检测出方格、点形、星形、平纹、斜纹等多类型纹理织物的疵点,并显著提高检测速度。

Fabric defect detection method based on improved fast weighted median filtering and K-means

In order to detect various defects in the production process of textured fabrics, a texture fabric defect detection method based on improved weighted median filtering and K-means clustering was proposed. Firstly, the fabric image was preprocessed by the improved weighted median filter to reduce the influence of texture information on the defect detection. At the same time, by assigning weights and pixels to the histogram dynamic data, the time to seek the median was effectively shortened to increase the execution speed. Then, the K-means algorithm was adopted to cluster the filtered fabric images, and the cluster centers of the fabric image defects and non-defects were calculated, thereby realizing the segmentation of the image defect regions. The experimental results show that the method can effectively detect the defects of various types of textured fabrics such as square, dot, star, plain, and twill and significantly increase the detection speed.