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Updated: Jul 1, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Industrial Printed Circuit Board Surface Defect Dataset for Object Detection.

Hao Yan1,2, Xiaoguang Yu3,4, Bifang Ma1

  • 1School of Electronic and Mechanical Engineering,Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing, 350300, Fujian, China.

Scientific Data
|June 29, 2026
PubMed
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This summary is machine-generated.

This study introduces PCB-IND, a real-world dataset for printed circuit board (PCB) surface defect detection. It features industrial images to improve automated optical inspection (AOI) model performance in manufacturing.

Area of Science:

  • Materials Science
  • Computer Vision
  • Manufacturing Engineering

Background:

  • Printed circuit board (PCB) defect detection is crucial for electronic manufacturing quality control.
  • Existing datasets lack real-world industrial conditions, including varied illumination and defect types.
  • Current methods struggle with the complexity of defects found in actual production lines.

Purpose of the Study:

  • To introduce PCB-IND, a large-scale, real-world dataset for PCB surface defect detection.
  • To provide a benchmark dataset that captures industrial complexities like non-uniform illumination and diverse defect scales.
  • To facilitate the development and evaluation of robust defect detection models for industrial applications.

Main Methods:

  • Collected 4,789 real-world images from an industrial automated optical inspection (AOI) system.

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  • Annotated 5,932 defect instances across eight typical surface defect categories.
  • Evaluated dataset usability with representative object detection models.

    • Main Results:

    • PCB-IND dataset preserves industrial imaging characteristics: non-uniform illumination, high contrast, and cross-scale defects.
    • The dataset exhibits a natural long-tail distribution of defects, reflecting real production scenarios.
    • Experimental results demonstrate the dataset's suitability for training and evaluating object detection models under industrial conditions.

    Conclusions:

    • PCB-IND offers a valuable real-world data resource for advancing industrial defect detection research.
    • The dataset supports research in early-stage process monitoring and defect repair decision-making.
    • It enables the development of more accurate and reliable automated optical inspection systems for PCB manufacturing.