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doctoral thesis
Deep learning-based methods for defect detection from ultrasound images
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:247036
Medak, Duje
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electronic Systems and Information Processing

Cite this document

Medak, D. (2022). Deep learning-based methods for defect detection from ultrasound images (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:247036

Medak, Duje. "Deep learning-based methods for defect detection from ultrasound images." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:247036

Medak, Duje. "Deep learning-based methods for defect detection from ultrasound images." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:247036

Medak, D. (2022). 'Deep learning-based methods for defect detection from ultrasound images', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:247036

Medak D. Deep learning-based methods for defect detection from ultrasound images [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 April 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:247036

D. Medak, "Deep learning-based methods for defect detection from ultrasound images", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:247036

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Metadata
TitleDeep learning-based methods for defect detection from ultrasound images
Title (croatian)Metode zasnovane na dubokom učenju za detekciju defekata iz ultrazvučnih slika
AuthorDuje Medak
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
Ultrasonic testing is a non-destructive evaluation (NDE) technique that is used to inspect the integrity of the material and check if there are any defects in its internal structure. The acquisition of ultrasonic data is already done in an automated fashion using robotic manipulators, but the analysis of the data is still done manually by specially trained experts. Manual analysis is subject to human errors especially when a large amount of data needs to be inspected. The goal of this doctoral dissertation is to develop deep learning-based methods that can be used to efficiently and reliably detect defects from ultrasonic images. Deep learning methods have been achieving great results in many image analysis tasks during the past few years. However, to develop a good deep learning-based method for defect detection, several problems must be solved. Some of the encountered challenges include a small dataset, noise and geometry signals, and extreme aspect ratios of the defects.
Abstract (croatian)
Ultrazvučno testiranje je tehnika nerazornog ispitivanja koja se koristi za ispitivanje kvalitete materijala i detekciju defekata unutar njegove strukture. Akvizicija ultrazvučnih podataka se već obavlja automatizirano korištenjem robotskih manipulatora, ali analizu prikupljenih podataka još uvijek ručno rade obučeni inspektori. Takva analiza podložna je ljudskoj pogrešci, pogotovo ako je količina podataka koju je potrebno pregledati velika. Cilj ove doktorske disertacije je razviti metode bazirane na dubokom učenju koje bi se mogle koristiti za efikasnu i pouzdanu detekciju defekata na ultrazvučnim slikama. Metode dubokog učenja zadnjih godina postižu odlične rezultate u raznim problemima vezanim za analizu slike. Međutim, kako bi se razvila dobra metoda za detekciju defekata bazirana na dubokom učenju, nekoliko problema mora biti riješeno. Neki od izazova koji se pojavljuju su mali skup podataka, šumoviti podaci, pojava signala uzrokovanih geometrijom i ekstremni omjeri oblika defekata.
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:168:247036
Promotion2022
Study programmeTitle: Computer Science
Study programme type: university
Study level: postgraduate
Academic / professional title: Doktor znanosti (Doktor znanosti)
Type of resourceText
Extentix, 107 str. : graf. prikazi , 30 cm.
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
RepositoryFER Repository
Created on2022-07-07 11:45:03

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