Naslov
Flaw Detection from Ultrasonic Images using YOLO and SSD

Autori
Posilović, Luka ; Medak, Duje ; Subašić, Marko ; Petković, Tomislav ; Budimir, Marko ; Lončarić, Sven

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) / Lončarić, Sven ; Bregović, Robert ; Carli, Marco ; Subašić, Marko - Dubrovnik : Institute of Electrical and Electronics Engineers (IEEE), 2019, 163-168

ISBN
978-1-7281-3140-5

Skup
11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)

Mjesto i datum
Dubrovnik, Hrvatska, 23.09.2019. - 25.09.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
image processing, image analysis, convolutional neural networks, ultrasonic imaging, non-destructive testing, automated flaw detection

Sažetak
Non-destructive ultrasonic testing (UT) of materials is used for monitoring critical parts in power plants, aeronautics, oil and gas industry, and space industry. Due to a vast amount of time needed for a human expert to perform inspection it is practical for a computer to take over that task. Some attempts have been made to produce algorithms for automatic UT scan inspection mainly using older, non-flexible analysis methods. In this paper, two deep learning based methods for flaw detection are presented, YOLO and SSD convolutional neural networks. The methods' performance was tested on a dataset that was acquired by scanning metal blocks containing different types of defects. YOLO achieved average precision (AP) of 89.7% while SSD achieved AP of 84.5%.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti

POVEZANOST RADA