U-Net based Zero-hour Defect Inspection of Electronic Components and Semiconductors

Florian Kälber, Florian Kälber, Okan Köpüklü, Nicolas Lehment, Gerhard Rigoll

Abstract

Automated visual inspection is a popular way of detecting many kind of defects at PCBs and electronic components without intervening in the manufacturing process. In this work, we present a novel approach for anomaly detection of PCBs where a U-Net architecture performs binary anomalous region segmentation and DBSCAN algorithm detects and localizes individual defects. At training time, reference images are needed to create annotations of anomalous regions, whereas at test time references images are not needed anymore. The proposed approach is validated on DeepPCB dataset and our internal chip defect dataset. We have achieved 0.80 and 0.75 mean Intersection of Union (mIoU) scores on DeepPCB and chip defect datasets, respectively, which demonstrates the effectiveness of the proposed approach. Moreover, for optimized and reduced models with computational costs lower than one giga FLOP, mIoU scores of 0.65 and above are achieved justifying the suitability of the proposed approach for embedded and potentially real-time applications.

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Paper Citation


in Harvard Style

Kälber F., Köpüklü O., Lehment N. and Rigoll G. (2021). U-Net based Zero-hour Defect Inspection of Electronic Components and Semiconductors.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 593-601. DOI: 10.5220/0010320205930601


in Bibtex Style

@conference{visapp21,
author={Florian Kälber and Okan Köpüklü and Nicolas Lehment and Gerhard Rigoll},
title={U-Net based Zero-hour Defect Inspection of Electronic Components and Semiconductors},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={593-601},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010320205930601},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - U-Net based Zero-hour Defect Inspection of Electronic Components and Semiconductors
SN - 978-989-758-488-6
AU - Kälber F.
AU - Köpüklü O.
AU - Lehment N.
AU - Rigoll G.
PY - 2021
SP - 593
EP - 601
DO - 10.5220/0010320205930601