Open AccessRobust image registration of printed circuit boards using improved SIFT‐PSO algorithm
Author(s) -
Dai Linhui,
Guan Qiao,
Liu Hong
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8274
Subject(s) - scale invariant feature transform , printed circuit board , computer science , image registration , artificial intelligence , affine transformation , particle swarm optimization , computer vision , image stitching , feature (linguistics) , image (mathematics) , algorithm , pattern recognition (psychology) , mathematics , linguistics , philosophy , pure mathematics , operating system
Printed circuit board (PCB) layout is becoming high density, high performance, light, and short. In the automatic PCB defect detection system, image registration of PCB plays an important role. However, most of the traditional registration methods are inefficient, and cannot cope with the problems of image distortion, affine, noise, and so on. To address this issue, the authors propose an improved scale invariant feature transform (SIFT) feature extraction algorithm combined with particle swarm optimisation (PSO) to register the images of PCB which placed on a conveyor belt. The advantage of the presented approach is that the registration results are more robust and efficient by optimising the existing PCB image matching framework. The experimental results on the proposed PCB datasets show that the speed of the proposed method (improved SIFT‐PSO) is faster than the traditional SIFT feature registration method, and the average computing time of processing single picture can be improved by 10 s, the registration accuracy can be improved by 3–4%. Compared with the experimental results of other algorithms, the root‐mean‐square error can be reduced to 0.5146 by using the proposed method. Thus, the proposed method (improved SIFT‐PSO) is more accurate and robust in real‐time inspection system of PCB.