Segmentation of Industrial Burner Flames: A Comparative Study from
  Traditional Image Processing to Machine and Deep Learning
Steven Landgraf Markus Hillemann Moritz Aberle Valentin Jung Markus Ulrich
In many industrial processes, such as power generation, chemical production,
and waste management, accurately monitoring industrial burner flame
characteristics is crucial for safe and efficient operation. A key step
involves separating the flames from the background through binary segmentation.
Decades of machine vision research have produced a wide range of possible
solutions, from traditional image processing to traditional machine learning
and modern deep learning methods. In this work, we present a comparative study
of multiple segmentation approaches, namely Global Thresholding, Region
Growing, Support Vector Machines, Random Forest, Multilayer Perceptron, U-Net,
and DeepLabV3+, that are evaluated on a public benchmark dataset of industrial
burner flames. We provide helpful insights and guidance for researchers and
practitioners aiming to select an appropriate approach for the binary
segmentation of industrial burner flames and beyond. For the highest accuracy,
deep learning is the leading approach, while for fast and simple solutions,
traditional image processing techniques remain a viable option.