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master's thesis
Učenje pod nadzorom korištenjem SSD i SVM metoda za nedestruktivna ispitivanja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:304113
Medak, Duje
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Medak, D. (2019). Učenje pod nadzorom korištenjem SSD i SVM metoda za nedestruktivna ispitivanja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:304113

Medak, Duje. "Učenje pod nadzorom korištenjem SSD i SVM metoda za nedestruktivna ispitivanja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:304113

Medak, Duje. "Učenje pod nadzorom korištenjem SSD i SVM metoda za nedestruktivna ispitivanja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:304113

Medak, D. (2019). 'Učenje pod nadzorom korištenjem SSD i SVM metoda za nedestruktivna ispitivanja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:304113

Medak D. Učenje pod nadzorom korištenjem SSD i SVM metoda za nedestruktivna ispitivanja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:304113

D. Medak, "Učenje pod nadzorom korištenjem SSD i SVM metoda za nedestruktivna ispitivanja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:304113

Metadata
TitleUčenje pod nadzorom korištenjem SSD i SVM metoda za nedestruktivna ispitivanja
Title (english)Supervised Learning Using SSD and SVM Methods for Nondestructive Testing
AuthorDuje Medak
MentorSven Lončarić (mentor)
Committee memberSven Lončarić (predsjednik povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberTomislav Petković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Glavni cilj ovog rada je ispitati mogućnost korištenja dviju metoda za detekciju objekata u svrhu detekcije defekata na slikama dobivenim ultrazvučnim testiranjem. Prva predložena metoda bazirana je na histogramu orijentacija gradijenata (HOG) i stroju potpornih vektora (SVM). Druga metoda bazirana je na dubokoj konvolucijskoj mreži Single Shot Detector (SSD). Na početku rada dan je uvod u nedestruktivna testiranja i objašnjen je princip rada učestalo korištenih tehnika ultrazvučnog testiranja. Prikazani su i načini reprezentacije podataka i dan je pregled postojećih metoda za automatsku detekciju defekata. Nakon detaljnog opisa predloženih metode prikazana je i njihova primjena u detekciji defekata na B-prikazima. Metode su testirane na 98 slika iz testnog skupa i kombinacija HOG-a i SVM-a postigla je prosječnu preciznost 23.5% dok je SSD modelom postignuta prosječna preciznost od 84.5%.
Abstract (english)
The main goal of this work was to test the usage of two object detection methods for the task of defect detection from images acquired in the process of ultrasonic testing. The first introduced method is based on the histogram of oriented gradients (HOG) and support vector machines (SVM). The second method is based on deep convolutional network Single Shot Detector. At the beginning of the work, an introduction to nondestructive testing is given and the principle of two commonly used ultrasonic testing techniques is explained. Various types of data representation are shown and overview of the previous work in the area of automatic defect detection of ultrasonic data is given. After providing detail description of the proposed methods, their application in the task of defect detection from B-scans is shown. The methods were tested on 98 test images and the combination of HOG and SVM achieved an average precision of 23.5% while SSD achieved average precision of 84.5%.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:304113
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 on2020-01-07 20:16:45