prikaz prve stranice dokumenta Generative adversarial networks for ultrasound image synthesis and analysis in nondestructive evaluation
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doctoral thesis
Generative adversarial networks for ultrasound image synthesis and analysis in nondestructive evaluation
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:751803
Posilović, Luka
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electronic Systems and Information Processing

Institutional repository: FER Repository

Cite this document

Posilović, L. (2022). Generative adversarial networks for ultrasound image synthesis and analysis in nondestructive evaluation (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:751803

Posilović, Luka. "Generative adversarial networks for ultrasound image synthesis and analysis in nondestructive evaluation." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:751803

Posilović, Luka. "Generative adversarial networks for ultrasound image synthesis and analysis in nondestructive evaluation." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:751803

Posilović, L. (2022). 'Generative adversarial networks for ultrasound image synthesis and analysis in nondestructive evaluation', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 16 February 2025, https://urn.nsk.hr/urn:nbn:hr:168:751803

Posilović L. Generative adversarial networks for ultrasound image synthesis and analysis in nondestructive evaluation [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2025 February 16] Available at: https://urn.nsk.hr/urn:nbn:hr:168:751803

L. Posilović, "Generative adversarial networks for ultrasound image synthesis and analysis in nondestructive evaluation", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:751803

Metadata
TitleGenerative adversarial networks for ultrasound image synthesis and analysis in nondestructive evaluation
Title (croatian)Generativne suparničke mreže za sintezu i analizu ultrazvučnih slika u nerazornim ispitivanjima
AuthorLuka Posilović
MentorSven Lončarić (mentor)
Committee memberMarko Subašić (predsjednik povjerenstva)
Committee memberZvonko Kostanjčar (član povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Electronic Systems and Information Processing)
Zagreb
Defense date and country2022-06-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Data Processing
Universal decimal classification
(UDC)		004 - Computer science and technology. Computing. Data processing
Abstract
Non-destructive ultrasound evaluation is a technique for flaw detection in materials. Among other things, it is used for monitoring key components of nuclear power plants, railways, and pipelines. Analysis of ultrasound data is performed by human inspectors manually analyzing acquired images. Such a process can be tedious and is highly dependent on the inspector's previous experience. Deep learning methods are hard to develop because of the lack of available data. The shortage also impacts training new human experts in this field, since they learn through experience. Data from real inspections can not be used because of non-disclosure agreements. On the other hand, blocks with synthetic flaws are expensive to produce. The goal of this work is to develop methods for defect detection. To improve the method’s performance, deep learning methods for generating additional synthetic images need to be developed. Generated data should be realistic and of high quality even to human experts in ultrasound evaluation. Additional data should be used to improve the performance of the deep learning defect detector.
Abstract (croatian)
Nedestruktivno ultrazvučno ispitivanje je metoda za detekciju pukotina u materijalima. Koristi se za praćenje kritičnih komponenata nuklearnih elektrana, željezničkih pruga, i cjevovoda. Analizu ultrazvučnih podataka najčešće ručno provode inspektori. Taj proces može biti zamoran i kvaliteta cijele inspekcije jako ovisi o prijašnjem iskustvu inspektora. Metode dubokog učenja je teško razviti zbog nedostatka podataka. Taj nedostatak također utječe na uvježbavanje novih inspektora u području, jer i oni uče iz iskustva. Podatci sa stvarnih inspekcija se ne mogu koristiti zbog raznih ugovora o povjerljivosti podataka. U drugu ruku, blokovi sa sintetski proizvedenim pukotinama je teško i skupo za proizvesti. Cilj ovog rada je razvoj metode za detekciju pukotina. Kako bi poboljšali rad metoda temeljenih na dubokom učenju, potrebno je razviti nove metode za generiranje dodatnih sintetskih slika. Generirani podatci moraju biti realistički i visoke kvalitete. čak i ekspertima za ultrazvučno ispitivanje. Dodatni podatci se također mogu koristiti za uneprijeđenje rada detektora pukotina temeljenog na dubokom učenju.
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:168:751803
Promotion2022
Study programmeTitle: Computer Science
Study programme type: university
Study level: postgraduate
Academic / professional title: Doktor znanosti (Doktor znanosti)
Type of resourceText
Extentix, 93 str. : graf. prikazi ; 30 cm.
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-07-07 12:20:03