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Improving Image Monitoring Performance for Underwater Laser Cutting Using a Deep Neural Network

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Abstract

We designed an efficient signal processing implemented with artificial intelligence using a deep neural network for image monitoring of underwater laser cutting for nuclear power plant dismantling. Monitoring images for underwater laser cutting with intense flames in turbid water are characterized by low visibility while pixel values of an image are distributed over the entire dynamic range. The visibility for underwater laser cutting operations was improved by widely stretching pixel value distribution to the full possible dynamic range after removing excessively dark or bright pixels that are far from the dominant pixel intensity distribution. Here, areas of intense flame where pixel values are close to saturation values are preserved. In addition, an efficiently designed look-up table increases contrast in cutting areas with intense flames, and an image acquisition method using the lowest pixel values in the latest frames reduces intermittent monitoring interference caused by the flames erupting in irregular patterns and flowing bubbles. A deep learning neural network trained with the designed signal processing datasets effectively improved the image monitoring performance in underwater laser cutting experiments.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF), Grant funded by the Korea Government (MIST) (No. RS-2022-00155395).

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Authors and Affiliations

  1. Korea Atomic Energy Research Institute, 111, Daedeokdaero 989Beon-Gil, Yuseong-Gu, Daejeon, 34057, South Korea

    Seung-Kyu Park, Ki-Hee Song, Seong Yong Oh, Jae Sung Shin & Hyunmin Park

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  1. Seung-Kyu Park
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  2. Ki-Hee Song
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  3. Seong Yong Oh
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Correspondence to Seung-Kyu Park.

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Park, SK., Song, KH., Oh, S.Y. et al. Improving Image Monitoring Performance for Underwater Laser Cutting Using a Deep Neural Network. Int. J. Precis. Eng. Manuf. 24, 671–682 (2023). https://doi.org/10.1007/s12541-023-00771-1

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