Analiza ultrazvučnih slika dubokim učenjem za nedestruktivna ispitivanja

master's thesis

• Title (english): Ultrasound Image Analysis Using Deep Learning for Nondestructive Testing
• Author: Branimir Filipović
• Mentor: Sven Lončarić (mentor)
• Committee member: Sven Lončarić (predsjednik povjerenstva)
• Committee member: Marko Subašić (član povjerenstva)
• Committee member: Tomislav Petković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2020-07-10, 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: NDT; C-snimci; duboko učenje; analiza slike; RetinaNet model
• Keywords (english): NDE; C-scans; deep learning; image analysis; RetinaNet model
• Language: croatian
• URN:NBN: urn:nbn:hr:168:445680
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 12:13:17