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Generating ultrasonic images indistinguishable from real images using Generative Adversarial Networks (CROSBI ID 300007)

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Posilović, Luka ; Medak, Duje ; Subašić, Marko ; Budimir, Marko ; Lončarić, Sven Generating ultrasonic images indistinguishable from real images using Generative Adversarial Networks // Ultrasonics, 119 (2021), 106610, 10. doi: 10.1016/j.ultras.2021.106610

Podaci o odgovornosti

Posilović, Luka ; Medak, Duje ; Subašić, Marko ; Budimir, Marko ; Lončarić, Sven

engleski

Generating ultrasonic images indistinguishable from real images using Generative Adversarial Networks

Ultrasonic imaging is widely used for non- destructive evaluation in various industry applications. Early detection of defects in materials is the key to keeping the integrity of inspected structures. Currently, there have been some attempts to develop models for automated defect detection on ultrasonic data. To push the performance of these models even further more data is needed to train deep convolutional neural networks. A lot of data is also needed for training human experts. However, gathering a sufficient amount of data for training is a challenge due to the rare occurrence of defects in real inspection scenarios. This is why inspection results heavily depend on the inspector’s previous experience. To overcome these challenges, we propose the use of Generative Adversarial Networks for generating realistic ultrasonic images. To the best of our knowledge, this work is the first one to show that a Generative Adversarial Network is able to generate images indistinguishable from real ultrasonic images. The most thorough statistical quality analysis to date of generated ultrasonic images has been conducted with the participation of human expert inspectors. The experimental results show that images generated using our Generative Adversarial Network provide the highest quality images compared to other published methods.

Non-destructive testing ; Ultrasonic testing ; Synthetic Data Generation ; Generative Adversarial Network ; Deep learning

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Podaci o izdanju

119

2021.

106610

10

objavljeno

0041-624X

1874-9968

10.1016/j.ultras.2021.106610

Povezanost rada

Računarstvo

Poveznice