为有效进行芯片标识的提取,提出一种基于灰狼优化算法(gray wolf optimization,GWO)的改进动态双阈值的Canny算子来进行芯片标识图像边缘提取.首先,从芯片标识生产环境复杂、图像干扰信息多的角度出发,对Canny算子的双阈值进行改进;其次,使用灰狼优化算法确定其高阈值选取;最后,将本文算法与传统Log、Prewitt、Roberts、Canny、Sobel算子进行实验比较,利用召回率和精确率等方法作了客观评估.实验结果表明,本文所提算法优于传统的边缘提取算法,提取准确度高,为后续识别打下了坚实基础.
Chip Marking Image Edge Detection Based on Improved Canny Operator of Grey Wolf Optimization Algorithm
To effectively extract chip marking,an improved dynamic dual threshold Canny operator based on gray wolf optimization(GWO)algorithm is proposed for edge extraction of chip marking images.Firstly,from the perspective of the complex image interference information in the chip marking production environment,the dual threshold of the Canny operator is improved.Secondly,the grey wolf algorithm is used to determine its high threshold selection;Finally,the algorithm proposed in this paper is experimentally compared with traditional Log operators,traditional Prewitt operators,traditional Roberts operators,traditional Canny operators,and traditional Sobel operators,and objective evaluations are conducted using methods such as recall and accuracy.The experimental results show that the algorithm proposed in this study is superior to traditional edge extraction algorithms,with high extraction accuracy,and lays a solid foundation for subsequent recognition tasks.
chip marking imageedge detectionimproved Canny operatorgray wolf optimization(GWO)
万方数据
维普
