Improving Real-Time Performance of U-Nets for Machine Vision in Laser Process Control

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.