Improving real-time performance of U-nets for machine vision in laser process control

• Autorzy: Dolata Przemysław , Reiner Jacek

Identyfikatory

DOI

10.15439/2019F190

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

Many industrial machine vision problems, particularly real-time control of manufacturing processes such as laser cladding, require robust and fast image processing. The inherent disturbances in images acquired during these processes makes classical segmentation algorithms uncertain. Among many convolutional neural networks introduced recently to solve such difficult problems, U-Net balances simplicity with segmentation accuracy. However, it is too computationally intensive for usage in many real-time processing pipelines. In this work we present a method of identifying the most informative levels of detail in the U-Net. By only processing the image at the selected levels, we reduce the total computation time by 80%, while still preserving adequate quality of segmentation.

Słowa kluczowe

EN

computer vision; convolutional neural nets; image segmentation; process control

PL

wizja komputerowa; splotowe sieci neuronowe; segmentacja obrazu; kontrola procesu

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 29--33
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Dolata Przemysław

• Wrocław University of Science and Technology, ul. Wyb. Wyspianskiego 27, 50-370 Wrocław, Poland

autor

Reiner Jacek

• Wrocław University of Science and Technology, ul. Wyb. Wyspianskiego 27, 50-370 Wrocław, Poland

Bibliografia

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Uwagi

1. Track 1: Artificial Intelligence and Applications

2. Technical Session: 14th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).