INDUSTRIAL COMPUTER VISION FOR AUTOMOTIVE QUALITY CONTROL
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F25%3A00390682" target="_blank" >RIV/68407700:21260/25:00390682 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.14311/NNW.2025.35.003" target="_blank" >https://doi.org/10.14311/NNW.2025.35.003</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/NNW.2025.35.003" target="_blank" >10.14311/NNW.2025.35.003</a>
Alternative languages
Result language
angličtina
Original language name
INDUSTRIAL COMPUTER VISION FOR AUTOMOTIVE QUALITY CONTROL
Original language description
Maintaining consistent product quality is a key challenge in automotive manufacturing, where high production volumes and product variability place significant demands on inspection processes. Industrial computer vision (ICV) offers an effective approach for automating visual quality control using modern image processing and deep learning techniques. This paper presents a case study of an ICV system deployed at Skoda Auto for automated inspection of automotive door ˇ components on a pre-assembly production line. The system integrates industrial cameras, edge processing devices, and neural network models trained on annotated production datasets. The paper describes the system architecture, dataset preparation, model training, and integration with production monitoring tools. The deployed system inspects several million components annually and demonstrates reliable defect detection performance under real manufacturing conditions. The study highlights the practical benefits of industrial computer vision for large-scale automotive quality control and outlines future development directions including digital twin integration and predictive analytics.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Network World
ISSN
1210-0552
e-ISSN
2336-4335
Volume of the periodical
35
Issue of the periodical within the volume
3
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
21
Pages from-to
37-57
UT code for WoS article
001738204900001
EID of the result in the Scopus database
2-s2.0-105035977878