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In recent years, Printed Circuit Boards (PCB) have become the backbone of alarge number of consumer electronic devices leading to a surge in theirproduction. This has made it imperative to employ automatic inspection systemsto identify manufacturing defects in PCB before they are installed in therespective systems. An important task in this regard is the classification ofdefects as either true or pseudo defects, which decides if the PCB is to bere-manufactured or not. This work proposes a novel approach to detect mostcommon defects in the PCBs. The problem has been approached by employing highlydiscriminative features based on multi-scale wavelet transform, which arefurther boosted by using a kernalized version of the support vector machines(SVM). A real world printed circuit board dataset has been used forquantitative analysis. Experimental results demonstrated the efficacy of theproposed method.

Pseudo vs. True Defect Classification in Printed Circuits Boards using Wavelet Features