关键词: 脉冲耦合神经网络, 链齿缺陷检测, 图像处理, 灰度跃变

Abstract: Traditional manual method of metal zipper defects detection has the features of inefficiency, poor stability and high false positive rate. To these problems, this paper presents a new detection method based on Pulse Coupled Neural Network（PCNN）and gray mutation detection. Depending on the characteristics of metal zipper image, the processing speed of image segmentation will be increased by improving the traditional PCNN, which can extract the feature images of zipper scoops by combining with morphology. A new proposed method can automatically distinguish the metal zipper scoops defects through the region pixel statistics and gray mutation detection. A detection system is designed for this experimental study. Experiment results show the detection method of this paper is quick, accurate and feasible.

Key words: Pulse Coupled Neural Networks（PCNN）, scoops defect detection, image preprocessing, gray level mutation