Defects and Components Recognition in Printed Circuit Boards Using Convolutional Neural Network

  • Leong Kean Cheong
  • Shahrel Azmin SuandiEmail author
  • Saimunur Rahman
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 547)


This paper introduces an automated components recognition system for printed circuit boards using Convolutional Neural Network (CNN). In addition to that, localization on the defects of the PCB components is also presented. In the first stage, a simple convolutional neural network-based component recognition classifier is developed. Since training a convolutional neural network from scratch is expensive, transfer learning with pre-trained models is performed instead. Pre-trained models such as VGG-16, DenseNet169 and Inception V3 are used to investigate which model suits the best for components recognition. Using transfer learning with VGG-16, the best result achieved is 99% accuracy with the capability of recognizing up to 25 different components. Following that, object localization is performed using faster region-based convolutional neural network (R-CNN). The best mean average precision (mAP) achieved for the defects localization system is 96.54%.


Printed circuit boards Convolutional neural network Automated vision inspection system Transfer learning Mean average precision 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Leong Kean Cheong
    • 2
    • 1
  • Shahrel Azmin Suandi
    • 1
    Email author
  • Saimunur Rahman
    • 2
  1. 1.School of Electrical & Electronic Engineering, Engineering CampusUniversiti Sains MalaysiaNibong TebalMalaysia
  2. 2.Vitrox Corporation, Berhad No. 85-ABayan LepasMalaysia

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