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Pregled bibliografske jedinice broj: 1159172

Automated Ultrasonic Testing of Materials based on C-scan Flaw Classification


Filipović, Branimir; Milković, Fran; Subašić, Marko; Lončarić, Sven; Petković, Tomislav; Budimir, Marko
Automated Ultrasonic Testing of Materials based on C-scan Flaw Classification // 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)
Zagreb, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2021. str. 230-234 doi:10.1109/ispa52656.2021.9552056 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1159172 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Automated Ultrasonic Testing of Materials based on C-scan Flaw Classification

Autori
Filipović, Branimir ; Milković, Fran ; Subašić, Marko ; Lončarić, Sven ; Petković, Tomislav ; Budimir, Marko

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

Izvornik
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) / - : Institute of Electrical and Electronics Engineers (IEEE), 2021, 230-234

Skup
12th International Symposium on Image and Signal Processing and Analysis (ISPA 2021)

Mjesto i datum
Zagreb, Hrvatska, 13.09.2021. - 15.09.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
non-destructive testing ; ultrasonic imaging ; image processing ; computer vision ; convolutional neural networks ; automated flaw classification

Sažetak
The analysis of the data in non-destructive ultrasonic testing of materials is a very time- intensive task. To alleviate the aforementioned strain on the human expert inspectors, a plethora of assisted analysis methods based on deep learning have been developed recently. However, most of these methods are based on the automated detection of flaws in A-scans and B-scans and therefore we propose a method based on the detection of flaws in C-scans that can reduce the complexity of manual detection of flaws in B- scans. The proposed method classifies each row of the C-scan based on whether it contains any flaws or not. Afterward, the positively classified rows are forwarded for further automated (and manual) inspection. The results show that the developed method significantly reduces the number of B-scans that have to be further analyzed.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Poveznice na cjeloviti tekst rada:

doi doi.org

Citiraj ovu publikaciju:

Filipović, Branimir; Milković, Fran; Subašić, Marko; Lončarić, Sven; Petković, Tomislav; Budimir, Marko
Automated Ultrasonic Testing of Materials based on C-scan Flaw Classification // 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)
Zagreb, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2021. str. 230-234 doi:10.1109/ispa52656.2021.9552056 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Filipović, B., Milković, F., Subašić, M., Lončarić, S., Petković, T. & Budimir, M. (2021) Automated Ultrasonic Testing of Materials based on C-scan Flaw Classification. U: 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) doi:10.1109/ispa52656.2021.9552056.
@article{article, author = {Filipovi\'{c}, Branimir and Milkovi\'{c}, Fran and Suba\v{s}i\'{c}, Marko and Lon\v{c}ari\'{c}, Sven and Petkovi\'{c}, Tomislav and Budimir, Marko}, year = {2021}, pages = {230-234}, DOI = {10.1109/ispa52656.2021.9552056}, keywords = {non-destructive testing, ultrasonic imaging, image processing, computer vision, convolutional neural networks, automated flaw classification}, doi = {10.1109/ispa52656.2021.9552056}, title = {Automated Ultrasonic Testing of Materials based on C-scan Flaw Classification}, keyword = {non-destructive testing, ultrasonic imaging, image processing, computer vision, convolutional neural networks, automated flaw classification}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Zagreb, Hrvatska} }
@article{article, author = {Filipovi\'{c}, Branimir and Milkovi\'{c}, Fran and Suba\v{s}i\'{c}, Marko and Lon\v{c}ari\'{c}, Sven and Petkovi\'{c}, Tomislav and Budimir, Marko}, year = {2021}, pages = {230-234}, DOI = {10.1109/ispa52656.2021.9552056}, keywords = {non-destructive testing, ultrasonic imaging, image processing, computer vision, convolutional neural networks, automated flaw classification}, doi = {10.1109/ispa52656.2021.9552056}, title = {Automated Ultrasonic Testing of Materials based on C-scan Flaw Classification}, keyword = {non-destructive testing, ultrasonic imaging, image processing, computer vision, convolutional neural networks, automated flaw classification}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Zagreb, Hrvatska} }

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