Segmentation of Industrial Burner Flames: A Comparative Study from Traditional Image Processing to Machine and Deep Learning

TL;DR

This paper compares traditional image processing, machine learning, and deep learning methods for segmenting industrial burner flames, providing insights for selecting appropriate techniques based on accuracy and speed.

Contribution

It offers a comprehensive comparison of multiple segmentation approaches on a benchmark dataset, highlighting the strengths and limitations of each method.

Findings

Deep learning achieves the highest segmentation accuracy.

Traditional image processing methods are faster and simpler.

Deep learning methods outperform others in accuracy.

Abstract

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.