为了精确评价纱线疵点的种类与个数,提出了一种融合空间模糊C-均值(FCM)聚类的纱线疵点检测算法。首先利用融合空间FCM聚类算法提取纱线条干;然后对纱线条干进行形态学开运算处理,以获取精确的纱线条干,并利用条干上下边缘点之间的像素个数计算纱线的直径与平均直径;最后根据纱线疵点标准判定纱线疵点的种类与个数。为了验证本算法的有效性和准确性,对多种不同线密度的纯棉纱线进行测试,并将测试结果与电容性纱疵分级仪的检测结果进行对比。结果表明,本算法与电容性的检测结果一致性较好,且价格低廉,不易受环境温度、湿度等因素的影响。

In order to accurately evaluate the types and number of yarn defects, an algorithm of yarn defects detection based on spatial fuzzy C-means (FCM) clustering is proposed in this paper. First, the spatial FCM clustering algorithm is used to extract the yarn strips. Then, morphological opening operation is performed on the yarn strips to obtain accurate yarn strips, and the number of pixels between the upper and lower edges of the yarn is used to calculate the measured diameter and average diameter of the yarn. Finally, the type and number of yarn defects are determined according to the standard of yarn defects. In order to verify the validity and accuracy of the algorithm, a variety of pure cotton yarns with different linear densities are tested, and experimental results are compared with the capacitive yarn defects classifier. The results show that the algorithm is in good agreement with the result of capacitance detection, and it is cheap and not easy to be affected by environmental temperature, humidity and other factors.