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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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A dataset for deep learning based detection of printed circuit board surface defect.

Shengping Lv1, Bin Ouyang2, Zhihua Deng3

  • 1School of Engineering, South China Agricultural University, No. 483, Wushan Road, Guangzhou, 510642, China. lvshengping@scau.edu.cn.

Scientific Data
|July 22, 2024
PubMed
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This study introduces DsPCBSD+, a large dataset for detecting printed circuit board (PCB) surface defects. This resource aims to accelerate deep learning model development for improved PCB quality control.

Area of Science:

  • Materials Science
  • Computer Science
  • Electrical Engineering

Background:

  • Printed circuit board (PCB) manufacturing involves potential surface defects impacting performance and requiring robust quality control.
  • Current defect detection methods need enhancement for efficiency and accuracy in complex manufacturing environments.

Purpose of the Study:

  • To develop a comprehensive and diverse dataset for training deep learning models for PCB surface defect detection.
  • To categorize PCB surface defects systematically to improve model generalization.

Main Methods:

  • Categorization of PCB surface defects into 9 distinct types based on morphology, cause, and location.
  • Development of the DsPCBSD+ dataset, comprising 10,259 images with 20,276 manually annotated defects using bounding boxes.

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Main Results:

  • Creation of the DsPCBSD+ dataset, a significant resource for PCB surface defect research.
  • The dataset covers 9 distinct defect categories, facilitating diverse model training.

Conclusions:

  • The DsPCBSD+ dataset is openly accessible to promote research and advancements in deep learning-based PCB surface defect detection.
  • Availability of this dataset is crucial for accelerating the development of more accurate and efficient automated quality control systems for PCBs.