关键词: 模板匹配, 高斯金字塔变换, 新粒子群优化算法, 自适应学习因子, 印刷电路板装配

Abstract: To improve the accuracy and real-time performance of object region detection in Printed Circuit Board Assembly (PCBA),a PCBA template matching algorithm combined with Gaussian pyramid transformation and new particle swarm optimization algorithm was proposed.The inversed Sigmod function was used to adjust the inertia weight during particle swarm iterating.Adaptive learning factor models of individual and group were constructed respectively.The particle would be adjusted by random momentum factor when it was trapped in local solution under the adaptive criterion.The originate image and the template image were transformed according to Gaussian pyramid with four layers.Coarse matching region of top layer of sub-image was found by proposed particle swarm optimization algorithm,which would generate a neighboring region by inverse transformation of Gaussian pyramid.The neighboring region was compared with corresponding template sub-image,and the matching result was obtained in the bottom layer.The experimental results showed that the proposed method had obviously accuracy and real-time performance in the application of PCBA template matching.

Key words: template matching, Gaussian pyramid transformation, new particle swarm optimization algorithm, adaptive learning factor, printed circuit board assembly