INDUSTRIAL COMPUTER VISION FOR AUTOMOTIVE QUALITY CONTROL

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

INDUSTRIAL COMPUTER VISION FOR AUTOMOTIVE QUALITY CONTROL

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

INDUSTRIAL COMPUTER VISION FOR AUTOMOTIVE QUALITY CONTROL

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

2336-4335

Svazek periodika

35

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

21

Strana od-do

37-57

Kód UT WoS článku

001738204900001

EID výsledku v databázi Scopus

2-s2.0-105035977878