Flaw Detection from Ultrasonic Images using YOLO and SSD (CROSBI ID 682519)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Posilović, Luka ; Medak, Duje ; Subašić, Marko ; Petković, Tomislav ; Budimir, Marko ; Lončarić, Sven
engleski
Flaw Detection from Ultrasonic Images using YOLO and SSD
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%.
image processing, image analysis, convolutional neural networks, ultrasonic imaging, non-destructive testing, automated flaw detection
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Podaci o prilogu
163-168.
2019.
objavljeno
10.1109/ISPA.2019.8868929
Podaci o matičnoj publikaciji
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)
978-1-7281-3140-5
1849-2266
Podaci o skupu
11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)
predavanje
23.09.2019-25.09.2019
Dubrovnik, Hrvatska