Enhancing Printed Circuit Board Defect Detection through Ensemble Learning

TL;DR

A comparative analysis reveals that the proposed ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects.

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.

Figures (9)

• Figure 1: Different types of defects identified on a PCB.
• Figure 2: An example of multiple defects displayed on a single PCB.
• Figure 3: An example illustrating the image binarization process.
• Figure 4: Samples of Data Augmentation for Binarized Image.
• Figure 5: The overview of EfficientDet-based PCB defects detection architecture. The architecture contains three components, including backbone, feature fusion, and class/box network. An ImageNet-pretrained EfficientNets is employed as the backbone network.