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A control chart for monitoring image processes based on convolutional neural networks

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  • Yarema Okhrin
  • Wolfgang Schmid
  • Ivan Semeniuk

Abstract

In this paper, the problem of monitoring image processes with spatially correlated pixels over time is considered. An exponentially weighted moving average (EWMA) control chart for monitoring such processes based on a convolutional neural network (CNN) is proposed. A comparison of its performance with a Hotelling's T2 control chart and with a control chart based on generalized likelihood ratio (GLR) approach is conducted through a simulation study. The new method outperforms other methods in most of the cases considered in the simulation study. A technique for mean intensity shift localization based on CNNs is proposed and evaluated.

Suggested Citation

  • Yarema Okhrin & Wolfgang Schmid & Ivan Semeniuk, 2025. "A control chart for monitoring image processes based on convolutional neural networks," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 79(1), February.
    • Handle: RePEc:bla:stanee:v:79:y:2025:i:1:n:e12366
    DOI: 10.1111/stan.12366
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