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A Flexible PCB Inspection System Based on Statistical Learning


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.

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

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Correspondence to Shang-Hong Lai.

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Liao, C., Lee, W. & Lai, S. A Flexible PCB Inspection System Based on Statistical Learning. J Sign Process Syst 67, 279–290 (2012). https://doi.org/10.1007/s11265-010-0556-8

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  • PCB
  • SVM
  • Automated visual inspection
  • Image classification
  • Defect classification
  • Image alignment