Detekcija grešaka na tiskanim pločicama korištenjem strojnog učenja

master's thesis

• Title (english): Printed Circuit Board Defect Detection Using Machine Learning
• Author: Mislav Ivančić
• Mentor: Marin Šilić (mentor)
• Committee member: Marin Šilić (predsjednik povjerenstva)
• Committee member: Klemo Vladimir (član povjerenstva)
• Committee member: Goran Delač (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2021-07-05, 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,... More te je vrednovan korištenjem tradicionalnih mjera. Na posljetku su prikazani rezultati detekcije te sama učinkovitost dobivenog modela. Less

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... More using traditional measures. Finally, the detection results and the efficiency of the obtained model were presented. Less

• Keywords: Strojno učenje; računalni vid; YOLO; vizualna inspekcija; tiskane pločice.
• Keywords (english): Machine learning; computer vision; YOLO; visual inspection; printed circuit boards.
• Language: croatian
• URN:NBN: urn:nbn:hr:168:407729
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 15:06:14