A deep learning approach for automated PCB defect detection: A comprehensive review

Md Fauadi, Muhammad Hafidz Fazli and Tay, Sui Jian and Yahaya, Saifudin Hafiz and Mohamed Noor, Ahamad Zaki and Saptari, Adi (2024) A deep learning approach for automated PCB defect detection: A comprehensive review. Multidisciplinary Reviews, 8 (1). pp. 1-14. ISSN 2595-3982

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Abstract

The ever-expanding market for electronic devices has significantly heightened the demand for high-quality printed circuit boards (PCBs). Even minor defects in PCBs can pose substantial safety risks for end-users. This article provides a comprehensive review on deep learning-based approaches for PCB defect detection. Our exploration covers various critical aspects, including the classification of PCB defects, automated vision inspection (AVI) techniques, object detection methodologies, and the widespread adoption of deep learning models. Specifically, we focus on the state-of-the-art approach known as region-based Fully Convolutional Network with Feature Pyramid Networks (FPN-RFCN). Additionally, we discuss effective data augmentation techniques and commonly used evaluation metrics in this domain. This review provides valuable insights for researchers, practitioners, and industry professionals engaged in PCB quality assurance.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Feature pyramid network, PCB, Defect detection, Image processing, Smart manufacturing
Divisions:	Faculty Of Industrial And Manufacturing Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	23 Jun 2025 07:01
Last Modified:	23 Jun 2025 07:01
URI:	http://eprints.utem.edu.my/id/eprint/28825
Statistic Details:	View Download Statistic

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