Naslov
Deep learning-based methods for defect detection from ultrasound images (Metode zasnovane na dubokom učenju za detekciju defekata iz ultrazvučnih slika)
(Deep learning-based methods for defect detection from ultrasound images)

Autori
Medak, Duje

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, doktorska disertacija

Fakultet
Fakultet elektrotehnike i računarstva

Mjesto
Zagreb

Datum
10.06

Godina
2022

Stranica
107

Mentor
Lončarić, Sven

Ključne riječi
ultrasound image analysis ; non-destructive evaluation ; automated defect detection ; object detection ; data augmentation ; image generation ; deep learning

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti

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