Enhancing Printed Circuit Board Defect Detection through Ensemble Learning

TL;DR

This paper presents an ensemble learning framework combining four different PCB defect detection models to significantly improve accuracy in identifying diverse defects, surpassing individual model performance.

Contribution

The study introduces a novel ensemble approach that integrates multiple state-of-the-art models for PCB defect detection, demonstrating improved accuracy over individual methods.

Findings

Ensemble framework achieves 95% defect detection accuracy.

Combining models outperforms individual detection methods.

Framework enhances PCB quality control processes.

Abstract

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-the-art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes.