Yarn hairiness affects yarn and fabric quality. The existing hairiness detection methods cannot discriminate crossover fibers or hairs. In order to accurately separate and detect crossover fibers, an algorithm of separating crossover hairs is proposed. By obtaining refined hair skeletons after image pretreatment, the positions of fiber intersection point were determined, and a hair information table according to the characteristics of the hair cross-point was constructed. After classifying each hair branch skeleton and screening out the hair common skeleton, the branch hair matching table by using the two end points of the true common hair skeleton adjacent to the hair branch was constructed. Through pairing the same cross-hair branch with the principle of the closest slope at the adjacent cross-end, each complete cross-hair skeleton was defined for hair count in a field of view. The detection results show that compared with the existing photoelectric hairiness detection instrument, the algorithm can realize the crossover hair separation, and calculate the length of complete crossover hairs and curved hairs with high accuracy. On average, the developed algorithm measures hair length about 11.1% longer than the manually measured results, while commercial apparatus would report hair length 62.5% shorter than the actual hair length.

纱线毛羽影响纱线和织物的质量。现有的毛羽检测方法无法区分交叉纤维或毛发。为了准确分离和检测交叉纤维，提出了一种分离交叉纤维的算法。通过图像预处理后得到细化的头发骨架，确定纤维交叉点的位置，并根据头发交叉点的特征构建头发信息表。对各发枝骨架进行分类并筛选出头发共同骨架后，利用与该发枝相邻的真实共同头发骨架的两个端点构建枝发匹配表。通过将同一十字丝分支按照相邻十字端斜率最近的原则进行配对，每个完整的十字线骨架都是根据视野中的头发数量来定义的。检测结果表明，与现有光电毛羽检测仪相比，该算法能够实现交叉毛发分离，并高精度地计算出完整交叉毛发和弯曲毛发的长度。平均而言，所开发的算法测量的头发长度比手动测量的结果长约 11.1%，而商业设备报告的头发长度比实际头发长度短 62.5%。