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...

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Veröffentlicht in:	2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) S. 163 - 168
Hauptverfasser:	Posilovic, Luka, Medak, Duje, Subasic, Marko, Petkovic, Tomislav, Budimir, Marko, Loncaric, Sven
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.09.2019
Schlagworte:	Acoustics automated flaw detection Computer architecture convolutional neural networks Discrete wavelet transforms image analysis image processing Inspection non-destructive testing Probes Testing Training ultrasonic imaging
ISSN:	1849-2266
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	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 %.
ISSN:	1849-2266
DOI:	10.1109/ISPA.2019.8868929