Journal of Signal Processing Systems

, Volume 67, Issue 3, pp 279–290 | Cite as

A Flexible PCB Inspection System Based on Statistical Learning

  • Chia-Te Liao
  • Wen-Hao Lee
  • Shang-Hong Lai


With the large variations in appearance for different kinds of defects in Printed Circuit Boards (PCBs), conventional rule-based inspection algorithms become insufficient for detecting and classifying defects. In this study, an automated PCB inspection system based on statistical learning strategies is developed. First, the partial Hausdorff distance is used to ascertain the positions of defects. Next, the defect patterns are categorized using the Support Vector Machine (SVM) classifier. Defects without regularities in appearance, which cannot be categorized, are identified through the regional defectiveness by comparing the block-wise probability distributions. Experimental results on a real visual inspection platform show that the proposed system is very effective for inspecting a variety of PCB defects.


PCB SVM Automated visual inspection Image classification Defect classification Image alignment 



This work was partially supported by the Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu, Taiwan.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan

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