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master's thesis
Detekcija grešaka na tiskanim pločicama korištenjem strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:407729
Ivančić, Mislav
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Ivančić, M. (2021). Detekcija grešaka na tiskanim pločicama korištenjem strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:407729

Ivančić, Mislav. "Detekcija grešaka na tiskanim pločicama korištenjem strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:407729

Ivančić, Mislav. "Detekcija grešaka na tiskanim pločicama korištenjem strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:407729

Ivančić, M. (2021). 'Detekcija grešaka na tiskanim pločicama korištenjem strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 03 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:407729

Ivančić M. Detekcija grešaka na tiskanim pločicama korištenjem strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 July 03] Available at: https://urn.nsk.hr/urn:nbn:hr:168:407729

M. Ivančić, "Detekcija grešaka na tiskanim pločicama korištenjem strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:407729

Metadata
TitleDetekcija grešaka na tiskanim pločicama korištenjem strojnog učenja
Title (english)Printed Circuit Board Defect Detection Using Machine Learning
AuthorMislav Ivančić
MentorMarin Šilić (mentor)
Committee memberMarin Šilić (predsjednik povjerenstva)
Committee memberKlemo Vladimir (član povjerenstva)
Committee memberGoran Delač (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-05, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu opisuju se metode računalnog vida za detekciju komponenata na tiskanim pločicama. Detaljno su obrađene metode Fast RCNN i YOLO. Za praktični dio rada odabrana je YOLO metoda zbog velike brzine detekcije. Korišten je alat Google Colabratory za pisanje Python i bash skripti, te zbog dostupnih resursa grafičkih kartica za samo treniranje modela. Korišten je i radni okvir darknet koji omogućava lakši rad sa YOLO arhitekturom. Nakon samostalnog prikupljanja podataka, model je treniran, te je vrednovan korištenjem tradicionalnih mjera. Na posljetku su prikazani rezultati detekcije te sama učinkovitost dobivenog modela.
Abstract (english)
This paper describes computer vision methods for detecting printed circuit board components. Fast RCNN and YOLO methods have been studied in detail. YOLO method was chosen for the practical part of the paper due to the high detection speed. Google Colabratory was used to write Python and bash scripts, and also because of the available graphics card resources. Also darknet framework was used for easier work with YOLO architecture. After data collection, the model was trained and evaluated using traditional measures. Finally, the detection results and the efficiency of the obtained model were presented.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:407729
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-13 15:06:14