Deep learning-based methods for defect detection from ultrasound images

doctoral thesis

• Title (croatian): Metode zasnovane na dubokom učenju za detekciju defekata iz ultrazvučnih slika
• Author: Duje Medak
• Mentor: Sven Lončarić (mentor)
• Committee member: Marko Subašić (predsjednik povjerenstva)
• Committee member: Zvonko Kostanjčar (član povjerenstva)
• Committee member: Zoran Kalafatić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; (Department of Electronic Systems and Information Processing); Zagreb
• Defense date and country: 2022-06-10, 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: ultrasound image analysis; non-destructive evaluation; automated defect detection; object detection; data augmentation; image generation; deep learning
• Keywords (croatian): analiza ultrazvučnih slika; nerazorno ispitivanje; automatska detekcija defekata; detekcija objekata; augmentacija slike; generiranje slika; duboko učenje
• Language: english
• URN:NBN: urn:nbn:hr:168:247036
• Promotion: 2022
• Study programme: Title: Computer Science; Study programme type: university; Study level: postgraduate; Academic / professional title: Doktor znanosti (Doktor znanosti)
• Type of resource: Text
• Extent: ix, 107 str. : graf. prikazi , 30 cm.
• File origin: Born digital
• Access conditions: Open access
• Created on: 2022-07-07 11:45:03