Detekcija napak v proizvodnji z uporabo algoritma YOLO : magistrsko delo

Babajić, Jovan

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Title:	Detekcija napak v proizvodnji z uporabo algoritma YOLO : magistrsko delo
Authors:	ID Babajić, Jovan (Author) ID Klančnik, Simon (Mentor) More about this mentor...
Files:	MAG_Babajic_Jovan_2025.pdf (4,83 MB) MD5: 9FAAF55D3592375CD8DED1EFAA77BF07
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FS - Faculty of Mechanical Engineering
Abstract:	Magistrsko delo preučuje uporabo algoritma You Only Look Once (YOLO) za zaznavanje napak na piškotkih v proizvodnih procesih. Ugotovitve kažejo, da modeli manjše velikosti učinkovito zaznavajo napake pri manjših podatkovnih naborih, medtem ko večji modeli zahtevajo večjo količino podatkov za optimalno delovanje. Implementacija YOLO algoritma za avtomatizirano zaznavanje napak prinaša izboljšano kakovost in učinkovitost proizvodnje ter zmanjšuje stroške. Avtomatizacija omogoča zaposlenim, da se osredotočijo na analizo podatkov, kar povečuje njihovo zadovoljstvo in vrednost. Delo poudarja pomembnost naprednih tehnologij pri spodbujanju inovacij in konkurenčne prednosti v industriji.
Keywords:	YOLO, zaznavanje napak, piškotek, avtomatizirana kontrola kakovosti, strojno učenje
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[J. Babajić]
Year of publishing:	2025
Number of pages:	1 spletni vir (1 datoteka PDF (XI, 75 f.))
PID:	20.500.12556/DKUM-91787
UDC:	658.56:004.85(043.2)
COBISS.SI-ID:	227999235
Publication date in DKUM:	03.03.2025
Views:	125
Downloads:	69
Metadata:
Categories:	KTFMB - FS
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Licences

License:	CC BY 4.0, Creative Commons Attribution 4.0 International

Link:	http://creativecommons.org/licenses/by/4.0/
Description:	This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:	05.02.2025

Secondary language

Language:	English
Title:	Detection of defects in production using the YOLO algorithm
Abstract:	This master's thesis examines the application of the You Only Look Once (YOLO) algorithm for defect detection in cookies within manufacturing processes. The findings indicate that smaller models effectively detect defects in smaller datasets, whereas larger models require a greater volume of data to achieve optimal performance. The implementation of the YOLO algorithm for automated defect detection enhances product quality and manufacturing efficiency while reducing costs. Automation enables employees to focus on data analysis, thereby increasing their job satisfaction and overall value. This study underscores the importance of advanced technologies in fostering innovation and competitive advantage within the industry.
Keywords:	YOLO, defect detection, cookie, automated quality control, machine learning

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