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

Ahmed Jawad Rashid, Mohammad Aman Ullah, Adiba Isfara, Nadim Ahmed, Md Mamun Mian, Md Mashur Shalehin

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Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

DefectAtlas-20: A real-world image dataset for joint material and surface-condition classification.Data in brief · 2026
Article
PCB-defect: An annotated dataset for surface defect detection in printed circuit boards.Data in brief · 2026
Article

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5 · Who and what money

Authors and funding

6 authors.

Ahmed Jawad RashidDepartment of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, Bangladesh.

Mohammad Aman UllahDepartment of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, Bangladesh.

Adiba IsfaraDepartment of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, Bangladesh.

Nadim AhmedDepartment of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, Bangladesh.

Md Mamun MianDepartment of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, Bangladesh.

Md Mashur ShalehinDepartment of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The PCB-Defect dataset was developed to advance automated defect detection in Printed Circuit Boards. This dataset presents a comprehensive collection of 230 annotated high-resolution images of single-layer PCBs, manufactured through a controlled laboratory process. The curated diversity in PCB layouts, defect types, image quality, and annotation density supports the development of robust computer vision models for defect detection. Each board was fabricated using chemical etching on FR4 substrates to introduce manufacturing defects, including missing pad, mouse bite, open circuit, short circuit, spur, and spurious copper. The defect types were embedded at the design phase, physically realized during etching, and confirmed post-manufacturing. Images range from 800 × 600 to 6000 × 4000 pixels, averaging 6.61 megapixels. Detailed bounding box annotations for all visible defect instances were produced using the Roboflow tool, resulting in a total of 1704 annotated defects across the dataset with about 7.4 annotation per image. Annotations follow the COCO (Common Objects in Context) JSON format, which includes detailed metadata and precise localization of each defect by category and bounding box coordinates. This dataset offers significant reuse potential for both academic and industrial research communities focusing on automated optical inspection, quality control, and transfer learning applications

Indexed as

Computer visionDeep learningFault diagnosisPrinted circuit boards

Identifiers

PMID41488494

DOI10.1016/j.dib.2025.112296

PMCPMC12756537

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.