Napredna pretraga

Pregled bibliografske jedinice broj: 1028056

Flaw Detection from Ultrasonic Images using YOLO and SSD


Posilović, Luka; Medak, Duje; Subašić, Marko; Petković, Tomislav; Budimir, Marko; Lončarić, Sven
Flaw Detection from Ultrasonic Images using YOLO and SSD // 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) / Lončarić, Sven ; Bregović, Robert ; Carli, Marco ; Subašić, Marko (ur.).
Dubrovnik: IEEE, 2019. str. 163-168 doi:10.1109/ISPA.2019.8868929 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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 : IEEE, 2019, 163-168

ISBN
978-1-7281-3140-5

Skup
11th International Symposium on Image and Signal Processing and Analysis

Mjesto i datum
Dubrovnik, Hrvatska, 23-25.09.2019

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


Projekt / tema
KK.01.2.1.01.0151

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

Citati