ChangeChip: A Reference-Based Unsupervised Change Detection for PCB Defect Detection
The usage of electronic devices increases, and becomes predominant in most aspects of life. Surface Mount Technology (SMT) is the most common industrial method for manufacturing electric devices in which electrical components are mounted directly onto the surface of a Printed Circuit Board (PCB). Al...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
13.09.2021
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Subjects | |
Online Access | Get full text |
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Summary: | The usage of electronic devices increases, and becomes predominant in most
aspects of life. Surface Mount Technology (SMT) is the most common industrial
method for manufacturing electric devices in which electrical components are
mounted directly onto the surface of a Printed Circuit Board (PCB). Although
the expansion of electronic devices affects our lives in a productive way,
failures or defects in the manufacturing procedure of those devices might also
be counterproductive and even harmful in some cases. It is therefore desired
and sometimes crucial to ensure zero-defect quality in electronic devices and
their production. While traditional Image Processing (IP) techniques are not
sufficient to produce a complete solution, other promising methods like Deep
Learning (DL) might also be challenging for PCB inspection, mainly because such
methods require big adequate datasets which are missing, not available or not
updated in the rapidly growing field of PCBs. Thus, PCB inspection is
conventionally performed manually by human experts. Unsupervised Learning (UL)
methods may potentially be suitable for PCB inspection, having learning
capabilities on the one hand, while not relying on large datasets on the other.
In this paper, we introduce ChangeChip, an automated and integrated change
detection system for defect detection in PCBs, from soldering defects to
missing or misaligned electronic elements, based on Computer Vision (CV) and
UL. We achieve good quality defect detection by applying an unsupervised change
detection between images of a golden PCB (reference) and the inspected PCB
under various setting. In this work, we also present CD-PCB, a synthesized
labeled dataset of 20 pairs of PCB images for evaluation of defect detection
algorithms. |
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DOI: | 10.48550/arxiv.2109.05746 |