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Active learning and novel model calibration measurements for automated visual inspection in manufacturing
ID
Rožanec, Jože Martin
(
Avtor
),
ID
Bizjak, Luka
(
Avtor
),
ID
Trajkova, Elena
(
Avtor
),
ID
Zajec, Patrik
(
Avtor
),
ID
Keizer, Jelle
(
Avtor
),
ID
Fortuna, Blaž
(
Avtor
),
ID
Mladenić, Dunja
(
Avtor
)
PDF - Predstavitvena datoteka,
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(1,47 MB)
MD5: 54DD3FF34971CD8A459B2AC072909F40
URL - Izvorni URL, za dostop obiščite
https://link.springer.com/article/10.1007/s10845-023-02098-0
Galerija slik
Izvleček
Quality control is a crucial activity performed by manufacturing enterprises to ensure that their products meet quality standards and avoid potential damage to the brand’s reputation. The decreased cost of sensors and connectivity enabled increasing digitalization of manufacturing. In addition, artificial intelligence enables higher degrees of automation, reducing overall costs and time required for defect inspection. This research compares three active learning approaches, having single and multiple oracles, to visual inspection. Six new metrics are proposed to assess the quality of calibration without the need for ground truth. Furthermore, this research explores whether existing calibrators can improve performance by leveraging an approximate ground truth to enlarge the calibration set. The experiments were performed on real-world data provided by Philips Consumer Lifestyle BV. Our results show that the explored active learning settings can reduce the data labeling effort by between three and four percent without detriment to the overall quality goals, considering a threshold of p = 0.95. Furthermore, the results show that the proposed calibration metrics successfully capture relevant information otherwise available to metrics used up to date only through ground truth data. Therefore, the proposed metrics can be used to estimate the quality of models’ probability calibration without committing to a labeling effort to obtain ground truth data.
Jezik:
Angleški jezik
Ključne besede:
active learning
,
smart manufacturing
,
probability calibration
,
artificial intelligence
,
machine learning
,
automated visual inspection
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FE - Fakulteta za elektrotehniko
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2024
Št. strani:
Str. 1963–1984
Številčenje:
Vol. 35, iss. 5
PID:
20.500.12556/RUL-158239
UDK:
658.5
ISSN pri članku:
1572-8145
DOI:
10.1007/s10845-023-02098-0
COBISS.SI-ID:
181386243
Datum objave v RUL:
30.05.2024
Število ogledov:
156
Število prenosov:
16
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Objavi na:
Gradivo je del revije
Naslov:
Journal of intelligent manufacturing
Skrajšan naslov:
J. intell. manuf.
Založnik:
Springer Nature
ISSN:
1572-8145
COBISS.SI-ID:
513191705
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
pametna proizvodnja
,
nadzor proizvodnje
,
vizualni nadzor
,
zagotavljanje kakovosti
,
proizvodnja
,
aktivno učenje
,
umetna inteligenca
,
strojno učenje
Projekti
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Financer:
EC - European Commission
Program financ.:
H2020
Številka projekta:
956573
Naslov:
Safe and Trusted Human Centric Artificial Intelligence in Future Manufacturing Lines
Akronim:
STAR
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