A comprehensive review of research on surface defect detection of PCBs based on machine vision
Zihan He, Yudong Lian, Yulei Wang, Zhiwei Lu
Abstract
Printed circuit board (PCB) is a crucial component of advanced electronic devices, and its quality control cannot be overlooked. This paper presents a comprehensive review of machine vision-based surface defect detection methods for PCBs, addressing the transition from traditional image processing to advanced deep learning techniques. With the increasing complexity of PCB designs and the demand for high-precision manufacturing, automated defect detection has become critical for quality control. The study introduces nine public datasets for PCB surface inspection and fourteen common types of PCB surface defects, and provides an overview of commonly used performance evaluation metrics in the field of PCB defect detection. This paper systematically analyzes three categories of detection approaches: image processing-based methods, machine learning-based classifiers, and deep learning architectures. Furthermore, the review provides a comparative evaluation of representative works for each method, revealing that deep learning-based methods achieve state-of-the-art performance, while image processing methods and traditional machine learning approaches remain valuable in resource-constrained scenarios. We believe that the Transformer architecture has transformative potential in handling global defect patterns, while emphasizing the need for lightweight model architectures through techniques like knowledge distillation. This paper also mentions hybrid methods and ensemble approaches, which leverage the advantages of different detection techniques to achieve defect detection, improve detection accuracy, and reduce the probabilities of false positives and false negatives. Key challenges such as class imbalance, computational complexity, and detection of micro-defects are discussed, along with future directions including self-supervised learning, multi-modal learning, 3D defect detection, and edge-cloud collaborative systems.