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Related Experiment Video

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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

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Published on: May 28, 2016

PCB-defect: An annotated dataset for surface defect detection in printed circuit boards.

Ahmed Jawad Rashid¹, Mohammad Aman Ullah¹, Adiba Isfara¹

¹Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, Bangladesh.

|January 5, 2026

Summary

This summary is machine-generated.

The PCB-Defect dataset offers 230 high-resolution images for automated defect detection in Printed Circuit Boards (PCBs). This resource aids in developing robust computer vision models for quality control and automated optical inspection.

Keywords:

Computer vision Deep learning Fault diagnosis Printed circuit boards

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Area of Science:

Computer Vision
Materials Science
Manufacturing Engineering

Background:

Automated Optical Inspection (AOI) is crucial for Printed Circuit Board (PCB) quality control.
Existing datasets may lack diversity in defect types, image quality, and annotation density, hindering robust model development.
Advancements in computer vision necessitate comprehensive datasets for training sophisticated defect detection algorithms.

Purpose of the Study:

To introduce the PCB-Defect dataset, a novel resource for advancing automated defect detection in PCBs.
To provide a diverse collection of annotated images to support the development of robust computer vision models.
To facilitate research in automated optical inspection, quality control, and transfer learning within the PCB manufacturing industry.

Main Methods:

Collected 230 high-resolution images of single-layer PCBs manufactured with controlled chemical etching.
Introduced specific manufacturing defects including missing pad, mouse bite, open circuit, short circuit, spur, and spurious copper.
Annotated 1704 defects using bounding boxes in COCO JSON format via the Roboflow tool.

Main Results:

The PCB-Defect dataset comprises 230 images with diverse PCB layouts, defect types, and image resolutions (800x600 to 6000x4000 pixels).
A total of 1704 defects were annotated, averaging 7.4 annotations per image, providing detailed localization and categorization.
Annotations adhere to the COCO JSON format, ensuring compatibility with standard computer vision frameworks.

Conclusions:

The PCB-Defect dataset is a valuable resource for training and evaluating computer vision models for automated PCB defect detection.
The dataset's diversity and detailed annotations support research in areas like automated optical inspection and quality control.
This resource has significant potential for reuse in both academic and industrial research, promoting advancements in PCB manufacturing quality assurance.