基于奇部Gabor滤波器的纱管余纱量检测

针对现有细络联设备中机械式纱管检测分拣装置存在的磨损部件和损伤管纱表面纱线问题，提出一种基于机器视觉的非接触式余纱量检测方法。将单帧图像中的若干纱管从背景中逐一定位分割，校正角度并提取包含有效前景点的感兴趣区域，利用经参数优化的奇部Gabor滤波器组强化管壁和纱线边缘，对滤波后图像进行连通域检查，分割出纱线区域。通过实验分析了感兴趣区域提取阈值对检测准确性的影响，并基于极大输出响应原则分析了滤波器参数设定规则。结果表明，波长与高斯分布标准差比值为4.808、高斯分布标准差与纱线直径比值为0.81、方向与纱线平行的奇部Gabor滤波器检测效果最为理想。

Inspection of remaining yarn on bobbin based on odd Gabor filters

In order to avoid the fraction caused by mechanical remaining yarn inspecting devices in combined spinning and winding frames and preserve the quality of surface yarn around bobbins, a non-contact algorithm based on computer vision designed for bobbin inspection was introduced. Each bobbin tube was detected and segmented from a single frame. The interest region(ROI) of a single bobbin was then selected from the background and adjusted to vertical state. An optimized odd Gabor filter group was implemented on the ROI to enhance boundaries between the tube and yarn. Finally, the yarn region was examined and confirmed by connectivity checking. In the next section, the influence of ROI extraction threshold on detection accuracy was analyzed by experiments. The optimization of determining rule of filter parameters was elaborated based on maximum output princeple.The reaults show that the oddGabor filterwith the ratio of wavelength to standard deviation of4.808, the ratioof standard deviation to diameter of 0.81 and the directionparallel to the yarn has the best effect on remaining yarn inspection.