Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (2): 60-65.doi: 10.16180/j.cnki.issn1007-7820.2020.02.011

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PCB Board Common Sorting Algorithm Based on Convolutional Neural Network

WANG Zhengjun,YAO Yiming,CHEN Long   

  1. School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2019-01-14 Online:2020-02-15 Published:2020-03-12
  • Supported by:
    National Natural Science Foundation of China(51475309);National Natural Science Foundation of China(61772163)


The feature points of the traditional PCB board sorting algorithm are manually extracted, and the pose calculation method is single, which has the disadvantages of complex pretreatment steps, high sample requirements, low pose accuracy and low applicability. To solve the above problems, a general sorting algorithm for PCB board based on convolution neural network was proposed in this paper. An improved CaffeNet network model was constructed to automatically learn the deep features of PCB boards in a large number of image data, completed the automatic recognition and classification of PCB boards. The modified RANSACS algorithm was used to improve the accuracy of feature point matching of ORB algorithm, and the least square method was conducted to calculate the angle difference between the image of the board and the image to be matched, so as to realize the rapid positioning of all kinds of PCB boards. The result showed that the sorting accuracy of the algorithm reached 99.35%, which had good accuracy and sorting efficiency.

Key words: CNN, PCBboard, featureextraction, CaffeNet, ORBalgorithm, RANSACSalgorithm, least squares

CLC Number: 

  • TP312