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<p>Printed Circuit Boards (PCBs) are fundamental components in nearly every electronic system, ranging from consumer products to space applications. Due to the sophistication and miniaturization of modern PCBs, fault-free production is critical. Manual inspection and image-processing- based methods in the early stages are typically not adequately efficient in terms of speed, precision, and scalability. With advancements in deep learning, particularly object detection models, the YOLO (You Only Look Once) family of models has emerged as one of the top real-time contenders in PCB defect inspection. This paper provides a detailed discussion on the progress of lightweight models from the YOLO family in PCB surface defect detection. It discusses the working principles of YOLO, its advantages in industrial inspection, and summarizes recent variants such as YOLO-WWBI, SCF-YOLO, GESC- YOLO, and AE-YOLO. These frameworks attempt to achieve a trade-off between detection accuracy and computational cost, making them suitable for deployment in edge and embedded devices. By comparing their architectural innovations, detection abilities, and applicability in real-world settings, this paper provides insights into emerging trends and recommends directions for future research on intelligent, real-time PCB inspection.</p>

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A Review of Lightweight YOLO-based Models for Real Time PCB Surface Defect Detection

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Chandrasekar Swetha,
  • Ravi Menaka,
  • Krishnan Suvitha

摘要

Printed Circuit Boards (PCBs) are fundamental components in nearly every electronic system, ranging from consumer products to space applications. Due to the sophistication and miniaturization of modern PCBs, fault-free production is critical. Manual inspection and image-processing- based methods in the early stages are typically not adequately efficient in terms of speed, precision, and scalability. With advancements in deep learning, particularly object detection models, the YOLO (You Only Look Once) family of models has emerged as one of the top real-time contenders in PCB defect inspection. This paper provides a detailed discussion on the progress of lightweight models from the YOLO family in PCB surface defect detection. It discusses the working principles of YOLO, its advantages in industrial inspection, and summarizes recent variants such as YOLO-WWBI, SCF-YOLO, GESC- YOLO, and AE-YOLO. These frameworks attempt to achieve a trade-off between detection accuracy and computational cost, making them suitable for deployment in edge and embedded devices. By comparing their architectural innovations, detection abilities, and applicability in real-world settings, this paper provides insights into emerging trends and recommends directions for future research on intelligent, real-time PCB inspection.