纬编针织物疵点的实时检测

摘要

In order to detect the weft knitted fabric defects automatically, a real-time system and algorithm for the detection were developed in which the hardware part included image acquisition, signal processing and illumination device, while proograming was performed by Halcon software. The kinds of weft knitted fabric defects were defined as irregular shape and linear shape according to the characteristics of the defects. The cellular neural network was applied for detecting the defects with obvious gray level distribution and irregular shape. The line detection based on Radon transform was first proposed to identify the defects according to their linear shape, because the gray level distribution of the image and background of such defects are very similar. The experiments indicated that the algorithm could effectively detect defects like hole, dropped stitch, fly, float, course mark and miss tuck associated with weft knitted fabrics.%为实现纬编针织物疵点的自动检测,探讨了一种实时检测系统的开发方案和算法研究.系统的硬件包括图像采集、数据信号处理和光源部分使用Halcon软件进行编程.根据纬编针织物疵点的形状特点,将疵点分为形状不规则疵点和线形状疵点2类.针对纬编针织物部分疵点在图像上灰度分布明显,但形状不规则的特点,使用细胞神经网络对疵点进行分割;对于灰度差异较小,却呈线形状分布的疵点,引入线检测的方法,使用Radon变换定位疵点的位置.实验表明,该算法可以有效地检测出破洞、漏针、飞花、跳纱、横路和花针等纬编针织物疵点.