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

TL;DR

This paper introduces a novel wavelet-based feature extraction and SVM classification method for distinguishing true defects from pseudo defects in printed circuit boards, enhancing automatic inspection accuracy.

Contribution

It proposes a new defect classification approach using multi-scale wavelet features and kernel SVMs, validated on real-world PCB data.

Findings

Effective detection of common PCB defects

High classification accuracy demonstrated

Method outperforms existing techniques

Abstract

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