Paper 2020/366
FICS-PCB: A Multi-Modal Image Dataset for Automated Printed Circuit Board Visual Inspection
Hangwei Lu, Dhwani Mehta, Olivia Paradis, Navid Asadizanjani, Mark Tehranipoor, and Damon L. Woodard
Abstract
Over the years, computer vision and machine learn- ing disciplines have considerably advanced the field of automated visual inspection for Printed Circuit Board (PCB-AVI) assurance. However, in practice, the capabilities and limitations of these advancements remain unknown because there are few publicly accessible datasets for PCB visual inspection and even fewer that contain images that simulate realistic application scenarios. To address this need, we propose a publicly available dataset, “FICS-PCB”, to facilitate the development of robust methods for PCB-AVI. The proposed dataset includes challenging cases from three variable aspects: illumination, image scale, and image sensor. This dataset consists of 9,912 images of 31 PCB samples and contains 77,347 annotated components. This paper reviews the existing datasets and methodologies used for PCB- AVI, discusses challenges, describes the proposed dataset, and presents baseline performances using feature engineering and deep learning methods for PCB component classification.
Note: Data link added.
Metadata
- Available format(s)
-
PDF
- Category
- Foundations
- Publication info
- Preprint. MINOR revision.
- Keywords
- PCB datasetAutomated Visual Inspection
- Contact author(s)
- qslvhw @ ufl edu
- History
- 2020-07-17: last of 2 revisions
- 2020-04-02: received
- See all versions
- Short URL
- https://ia.cr/2020/366
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2020/366,
author = {Hangwei Lu and Dhwani Mehta and Olivia Paradis and Navid Asadizanjani and Mark Tehranipoor and Damon L. Woodard},
title = {{FICS}-{PCB}: A Multi-Modal Image Dataset for Automated Printed Circuit Board Visual Inspection},
howpublished = {Cryptology {ePrint} Archive, Paper 2020/366},
year = {2020},
url = {https://eprint.iacr.org/2020/366}
}
