prikaz prve stranice dokumenta Analiza ultrazvučnih slika dubokim učenjem za nedestruktivna ispitivanja
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master's thesis
Analiza ultrazvučnih slika dubokim učenjem za nedestruktivna ispitivanja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:445680
Filipović, Branimir
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Filipović, B. (2020). Analiza ultrazvučnih slika dubokim učenjem 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:168:445680

Filipović, Branimir. "Analiza ultrazvučnih slika dubokim učenjem za nedestruktivna ispitivanja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:445680

Filipović, Branimir. "Analiza ultrazvučnih slika dubokim učenjem za nedestruktivna ispitivanja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:445680

Filipović, B. (2020). 'Analiza ultrazvučnih slika dubokim učenjem za nedestruktivna ispitivanja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 03 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:445680

Filipović B. Analiza ultrazvučnih slika dubokim učenjem za nedestruktivna ispitivanja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 June 03] Available at: https://urn.nsk.hr/urn:nbn:hr:168:445680

B. Filipović, "Analiza ultrazvučnih slika dubokim učenjem za nedestruktivna ispitivanja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:445680

Metadata
TitleAnaliza ultrazvučnih slika dubokim učenjem za nedestruktivna ispitivanja
Title (english)Ultrasound Image Analysis Using Deep Learning for Nondestructive Testing
AuthorBranimir Filipović
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 country2020-07-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U nuklearnoj industriji vrlo je bitno u što kraćem roku naći eventualne defekte u materijalu, kako bi nuklearna elektrana što prije nastavila s radom. Takod̄er, izuzetno je bitno koristiti metode nedestruktivnog ispitivanja, s obzirom na to da su dijelovi jako skupi, a jedna od najpopularnijih je ultrazvučno ispitivanje. Te defekte traže ljudi, no med̄utim vrlo se rijetko neki nad̄u, a sam proces je vrlo mukotrpan. Stoga se koriste duboke neuronske mreže, kako bi se negativni primjeri što prije eliminirali. C-snimci su vrlo korisni za analizu, s obzirom na to da otkrivaju „3. dimenziju“ defekata, te se stoga ovaj Rad bavi automatskom detekcijom defekata korištenjem RetinaNet modela s ResNet50 backbone-om. Dobiveni rezultati imaju prostora za napredak, vjerojatno zbog prevelike kompleksnosti RetinaNet modela, te je potrebno daljnje istraživanje u ovom području.
Abstract (english)
In the nuclear industry it’s very important to find eventual defects in the material as soon as possible, to keep the halting of the nuclear power plant to the minimum. In addition to that, nondestructive evaluation methods are critical, due to the price of the parts used, and one of the most popular ones is ultrasonic testing. These defects are human-searched, but they are very rarely found, and the process is very exhausting. That’s why deep neural networks are being used, to eliminate the negative examples quickly. C-scans are very useful for analysis, due to the fact that they reveal "the 3. dimension" of defects, and that’s why this Thesis is doing automatic detection using the RetinaNet model with ResNet50 backbone. The end results have space for improvements, probably because the RetinaNet model is too complex, and therefore further research is necessary.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:445680
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 12:13:17