基于机器视觉的筒子纱缺陷在线检测系统

为提高筒子纱检测过程的自动化程度，设计了一种基于机器视觉的筒子纱缺陷在线检测系统。该系统由2个工业相机、条形LED光源、对照式光电开关和计算机组成。首先，相机与同步光源分时采集筒子纱顶面和侧面过曝模式及正常模式图像。然后通过对顶面过曝图像自适应分割来定位筒子纱中心。其次，通过极坐标变换展开顶面正常图像。最后，在顶面展开图中，分别利用垂直方向边缘分布的投影特征、纹理及强度一致性、局部方向直方图纹理识别菊花芯、多源纱和网纱缺陷；在筒子纱侧面图中，通过投影法快速确定边界位置，并通过轮廓拟合程度识别多层台缺陷。结果表明，该系统可实时识别多层台、网纱、菊花芯、多源纱等筒子纱缺陷，具有较好的检测效果。

On-line yarn cone defects detection system based on machine vision

In order to improve the degree of automation in the detection process of yarn cone, this paper proposed an on-line detection system for detecting yarn cone defects a on thetop surface and sides. The system is composed of two industrial cameras,an LED strip light, a photoelectric sensor and a computer. Firstly, the top images under the overexposure mode and normal mode, the side images under the overexposure modewere time-sharing collected by the camera and light combinations. Secondly, for the top overexposure image, the center of the yarn cone was located inby applying adaptive segmentation method. Thirdly, for the top image of normal mode, the transformed image was carried out after polar coordinate transformation, and the curly core yarn defect, the multi-source yarn defect and the net yarn defect were respectively detected based on analyzing the projective features of vertical edge distribution, analyzing texture and intensity distribution consistency, and local texture direction histogram. Finally, for the side overexposed image, the boundary position was quickly located by projection method, and then the multi-layer defect was determined by analyzing fitting degree of the contours. The experiment results show that the proposed system can identify some kinds of yarn cone defects, including muilti-layer, net yarn, curly core yarn and muilti-source yarn, withhigh detecting accuracy.