A method for inspecting the cleanliness of spinneret holes

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.