At present, computer vision system is widely used for the cleanliness inspection for spinneret holes, but it has a high misjudgment rate for many holes with small dirt. In this paper, a method is proposed to improve the accuracy of the cleanliness inspection. The method has four sequential phases. First, the closed contour curve of a standard hole and its curvature are extracted. Based on double-threshold segmentation of the contour curve, line and arc segments are segmented to generate a closed piecewise curve model. Second, the model is fitted to the closed contour curve L1 of the hole to be inspected based on a nonlinear least squares principle, and the distance curve that represents the shortest distance between the closed piecewise curve L2 constructed from the aligned model and L1 is calculated based on the nearest neighbor search algorithm. Third, the dirt detection curve L3 is generated from the distance curve weighted by a blended weighting curve. Final, based on a global threshold combining with unevenness elimination of L3, the cleanliness index is calculated based on the segmentation and location of dirt, and is used to judge whether the hole is qualified or not. The experimental results of four databases demonstrate that, the proposed method provides better performance compared with the traditional method.

中文翻译：

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一种检查喷丝孔清洁度的方法

目前，计算机视觉系统广泛用于喷丝孔的清洁度检测，但对于许多小脏污的孔，其误判率很高。本文提出了一种提高清洁度检测精度的方法。该方法有四个连续的阶段。首先，提取标准孔的闭合轮廓曲线及其曲率。基于轮廓曲线的双阈值分割，对直线段和弧段进行分割，生成封闭的分段曲线模型。其次，基于非线性最小二乘原理将模型拟合到待检孔的闭合轮廓曲线L1上，计算出表示对齐模型构建的闭合分段曲线L2与L1之间最短距离的距离曲线基于最近邻搜索算法。第三，污垢检测曲线L3是根据由混合加权曲线加权的距离曲线生成的。最后，基于全局阈值结合L3的不均匀消除，根据污垢的分割和位置计算清洁度指数，用于判断孔是否合格。四个数据库的实验结果表明，与传统方法相比，所提出的方法具有更好的性能。