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

S. Landgraf1,**footnotemark: * M. Hillemann1 M. Aberle2 V. Jung2 M. Ulrich1 1,
2Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of
Technology (KIT), Germany -
1 (steven.landgraf, markus.hillemann<EMAIL_ADDRESS>
2 (moritz.aberle<EMAIL_ADDRESS>

###### 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.†††*Corresponding author

###### keywords:

Segmentation, Industrial Burner Flames, Image Processing, Machine Learning,
Deep Learning.

##  1 Introduction

††footnotetext: †Our modified version of the used dataset:
https://publikationen.bibliothek.kit.edu/1000159497

Industrial combustion processes play a critical role in many industrial
processes, including power generation, chemical production, and waste
management. To ensure safe and efficient operation, it is essential to
accurately monitor the characteristics of the burner flames, such as their
shape, size, and temperature. A key step in camera-based monitoring involves
separating the flames from the background through segmentation (Großkopf et
al.,, 2021; Landgraf et al.,, 2022).

Traditionally, binary segmentation has been performed using image processing
techniques such as thresholding, edge detection, region growing, and
morphological operations (Steger et al.,, 2018). While these techniques can be
effective, they often require deep knowledge about the application,
significant manual tuning, and may not generalize well to new data.

In recent years, deep learning has emerged as a powerful data-driven approach
for segmentation tasks. These methods can learn meaningful features and
patterns from training data, which generalize well to the real application,
and therefore achieve higher accuracy than traditional techniques (Long et
al.,, 2015). However, they require large amounts of training data, which are
costly to label, and are typically computationally more expensive.

In this work, we present a comparative study of traditional image processing
techniques, traditional machine learning-based methods, and deep learning
approaches for binary segmentation of industrial burner flames in grayscale
images. Our study provides insights into the strengths and limitations of each
approach and can help guide researchers and practitioners in selecting the
most appropriate method for their specific application.

Figure 1: An overview of our comparative study. We compare seven different
approaches for the binary segmentation of industrial burner flames: Global
Thresholding, Region Growing, Support Vector Machines, Random Forest,
Multilayer Perceptron, U-Net, and DeepLabV3+.

##  2 Related Work

The following related work focuses on flame segmentation and is divided into
three categories: i) Traditional image processing methods, ii) traditional
machine learning methods, and iii) deep learning methods. With traditional
image processing methods, we refer to methods that use hand-crafted features
and rule-based decisions to distinguish between flame and background.
Traditional machine learning methods, on the other hand, refer to data-driven
methods like Support Vector Machines (SVM), Random Forest (RF), and Multilayer
Perceptron (MLP) which work with hand-crafted features. Finally, by deep
learning methods, we mean state-of-the art methods for image segmentation with
modern deep neural network-based architectures. This section only covers
related work regarding the segmentation of flames in images. Classification
and detection approaches are not covered to limit the scope.

Traditional Image Processing. Early approaches for flame segmentation use
traditional image processing techniques (Celik et al.,, 2007; Zhang et al.,,
2009; Fan et al.,, 2014; Wang et al.,, 2014; Matthes et al.,, 2019; Li and
Wang,, 2020). These methods rely on different hand-crafted features such as
color (Celik et al.,, 2007; Zhang et al.,, 2009; Wang et al.,, 2014; Li and
Wang,, 2020) and geometrical characteristics like area, roundness and contour
fluctuation (Zhang et al.,, 2009). Several of these methods use image
sequences or videos of the flames to determine time-dependent features like
color and area changes (Zhang et al.,, 2009; Wang et al.,, 2014; Matthes et
al.,, 2019; Li and Wang,, 2020). Some methods distinguish between flame and
background by using empirically determined thresholds for combinations of
these features (Zhang et al.,, 2009; Wang et al.,, 2014) or determine the
thresholds automatically using Otsu’s method (Matthes et al.,, 2019). Others
combine the features with fuzzy logic (Celik et al.,, 2007) or a Bayesian
model (Li and Wang,, 2020). Fan et al., (2014) choose a different approach and
use a level set method to detect the contour of the flame.

Traditional Machine Learning. Many of the traditional machine learning methods
for flame segmentation also use hand-crafted features like those described
above. However, these methods do not decide based on rules created by the
human developer, but create their own set of rules based on the data. In
addition to some of the features described above, Borges and Izquierdo, (2010)
use surface coarseness, boundary roughness and skewness as geometrical
characteristics and a Bayesian Classifier flame segmentation. Zhao et al.,
(2011) and Jamali et al., (2013) also use image sequences and time-dependent
features and then perform segmentation with a SVM, where Zhao et al., (2011)
takes into account other texture features such as entropy and contrast.
Recently, Liang et al., (2022) proposed a two step segmentation method. In the
first step, they determine the feature importance of different geometric,
texture and color features with a RF, and in the second step they use the five
most important features as input neurons of a MLP that distinguishes between
flame and background.

Deep Learning. Since the breakthrough of deep learning methods in many areas
of image processing, they have also been applied for flame segmentation. These
methods represent the current state-of-the-art in terms of segmentation
quality. In comparison to traditional machine learning methods, they no longer
rely on hand-crafted features, they learn the features best suited to separate
foreground and background through training. In this regard, Zhong et al.,
(2018) is a hybrid method, since it first determines candidate regions for
flames based on a threshold on color values and then uses a Convolutional
Neural Network (CNN) to classify the candidate regions into flame or
background. Großkopf et al., (2021) introduce a pure CNN-based method to
segment industrial burner flames in images. They not only focus on binary
segmentation of flames, but also perform multi-class segmentation. They also
provide a public image dataset with ground truth labels. Recently, Landgraf et
al., (2022) analyzed several self-supervised learning methods as pre-training
for the segmentation of flames. They show how the required amount of training
data can be significantly reduced without decreasing the quality of the
segmentation. Most recently, Wang et al., (2022) compared four different deep
learning methods, namely Fully Convolutional Network (Long et al.,, 2015),
U-Net (Ronneberger et al.,, 2015), PSPNet (Zhao et al.,, 2017) and DeepLabV3+
(Chen et al., 2018a, ) on the segmentation of forest fires in images captured
by UAVs. Their analysis shows that U-Net achieves the best performance, though
also having the slowest inference time. Compared to that, DeepLabV3+ is
slightly faster but also yields slightly worse results. In contrast, the FCN
and PSPNet models have shown to be less suitable because of lower scores for
the performance metrics.

##  3 Methodology

In the following, we provide an overview of our comparative study, as well as
explain the examined traditional image processing, traditional machine
learning and deep learning methods in more detail.

###  3.1 Overview

In contrast to the related work on flame segmentation and as Figure 1 shows,
we provide a comprehensive study that covers methods from traditional image
processing to traditional machine learning and modern deep learning. Our goal
is to provide helpful insights and guidance for researchers and practitioners
aiming to select an appropriate approach for the binary segmentation of
industrial burner flames and similar applications.

For our study, we compare seven popular binary segmentation approaches:

  1. 1.

Global Thresholding (GTH) (Lee et al.,, 1990),

  2. 2.

Region Growing (RG) (Adams and Bischof,, 1994),

  3. 3.

Support Vector Machines (SVM) (Hearst et al.,, 1998),

  4. 4.

Random Forest (RF) (Breiman,, 2001),

  5. 5.

Multilayer Perceptron (MLP) (McClelland et al.,, 1987),

  6. 6.

U-Net (Ronneberger et al.,, 2015),

  7. 7.

DeepLabV3+ (DL3+) (Chen et al., 2018b, ).

The hyperparameters of the traditional image processing techniques, GTH and
RG, as well as the traditional machine learning methods RF, SVM, and MLP, are
tuned on the training dataset. On the other hand, all of the deep learning
models are trained on the same fixed set of hyperparameters to limit the
computational cost. In return, we evaluate the effect of training from scratch
in comparison to fine-tuning from ImageNet pre-training and estimate the
impact of the model size, through training both U-Net and DL3+ with three
different backbones each.

###  3.2 Traditional Image Processing

Traditional image processing offers an easy and effective approach for binary
segmentation if they are adapted for the underlying application. This is why
we explore GTH and RG as a baseline for the more sophisticated data-driven
methods. We implemented both GTH and RG with HALCON (MVTec Software GmbH,,
2023).

Global Thresholding. GTH is a widely-used method for performing binary image
segmentation, where two threshold values are selected to separate the
foreground objects from the background. In our case, each pixel is classified
as industrial burner flame based on the following condition:

$T_{L}\leq g\leq T_{U},$ (1)

where $g$ is the gray value of the respective pixel between $0$ and $255$,
$T_{L}$ is the lower, and $T_{U}$ is the upper threshold. To determine the
optimal values for the lower and upper thresholds, we performed a grid search
on the training dataset.

Region Growing. RG is another traditional image processing technique for
binary segmentation, which can start at any pixel in the image. It then grows
the segmented region by including neighboring pixels that satisfy certain
criteria. In our case, a pixel is added to the region if the following
criterion is met:

$|r_{\overline{g}}-g|\leq T,$ (2)

where $r_{\overline{g}}$ is the mean gray value of the region, $g$ is the gray
value of the respective pixel, and $T$ is the chosen threshold. We segment the
background with the RG and invert the segmentation results instead of
segmenting the flames directly. This results in a better performance because
industrial burner flames are not always coherent. For the starting pixel, we
choose the lowest gray value in every given image and to determine the optimal
value of the threshold, we performed a grid search on the training dataset.

###  3.3 Traditional Machine Learning

In addition to the aforementioned traditional image processing techniques, we
also explored three traditional machine learning approaches: SVM, RF, and MLP.
We implemented all of them in Python with Scikit-learn (Pedregosa et al.,,
2011), Scikit-image (Van der Walt et al.,, 2014) and OpenCV (Bradski et al.,,
2000).

Figure 2: Example of the used basic multiscale features and Moravec corners
from Scikit-image for a single image with the ground truth label’s contour
overlaid in green.

Features. To improve the performance of the traditional machine learning
classifiers, we extract 23 hand-crafted features from the images. Like Figure
2 shows, these include 20 intensity, texture, edge, and corner features
obtained using basic multiscale features and Moravec corners from Scikit-
image. For smoothing the image with a Gaussian kernel, we used standard
deviations from 1.0 to 30.0. We also use OpenCV to build three median features
with square filters of sizes 51, 101, and 151. All of the features are
extracted pixelwise and as a consequence, the classification is performed on
each pixel individually.

Support Vector Machines. SVM is a popular algorithm for binary segmentation
that works by finding the optimal hyperplane that separates the positive and
negative examples with the largest possible margin. For hyperparameter
optimization, we performed a grid search on the training dataset to determine
the optimal values for the regularization parameter and the kernel function.

Random Forest. RF is an ensemble learning method that combines multiple
decision trees to improve the performance of the classifier. Thereby, each
decision tree is trained on a random subset of the dataset. For hyperparameter
optimization, we performed a grid search on the training dataset to determine
the optimal values for the number of decision trees, the maximum depth of the
trees, and the maximum number of training images for each tree.

Multilayer Perceptron. MLP is a basic neural network that consists of multiple
layers of interconnected nodes that can be utilized for binary segmentation.
Hereby, each node applies a nonlinear activation function to the weighted sum
of its inputs. Following the previous machine learning classifiers, we used
the 23 extracted features as input for the MLP. To optimize the
hyperparameters of the MLP, we performed a grid search on the training dataset
using a range of possible values for the number of hidden layers, the number
of nodes in each layer and the learning rate.

###  3.4 Deep Learning

Aside from traditional image processing techniques and traditional machine
learning methods, we also explore deep learning approaches for binary
segmentation of industrial burner flames. Both U-Net and DL3+ were implemented
with PyTorch (Paszke et al.,, 2019).

U-Net. U-Net is a fully convolutional neural network that uses a contracting
path to capture the image context and a symmetric expanding path to achieve
precise localization. The resulting encoder-decoder architecture has a
U-shape, which utilizes skip connections that allow information to be
propagated from the encoder to the decoder. The U-Net was first introduced for
medical image segmentation (Ronneberger et al.,, 2015).

DeepLabV3+. DeepLabV3+ is a fully convolutional neural network that uses
atrous (or dilated) convolutions within the atrous spatial pyramid pooling
(ASPP) module to capture multi-scale contextual information. It builds upon
the encoder-decoder architecture by fusing high-level ASPP features with low-
level features from earlier layers in the network (Chen et al., 2018b, ).

Backbones and Initialization. In order to estimate the impact of the model
size, we train both U-Net and DL3+ with three different backbones:

  1. 1.

MobileNetV3 Small (MN) (Howard et al.,, 2019),

  2. 2.

ResNet-18 (RN18) (He et al.,, 2016),

  3. 3.

ResNet-101 (RN101) (He et al.,, 2016).

Additionally, we evaluate the impact of the initialization for every model by:

  1. 1.

Training from scratch, i.e. with random weights (R),

  2. 2.

Training from ImageNet (I) (Deng et al.,, 2009).

Implementation Details. We train all the deep learning models with a binary
cross-entropy loss and employ a Stochastic Gradient Descent (SGD) optimizer
based on Robbins and Monro, (1951) with an initial learning rate of 0.01,
momentum of 0.9, and weight decay of 0.0005 as optimizer-specific
hyperparameters. During training, the learning rate decays based on:

$lr=lr_{initial}\cdot(1-\frac{iteration}{total\>iterations})^{0.9},$ (3)

where $lr$ is the current learning rate, and $lr_{initial}$ is the initial
learning rate. All models are trained for 25 epochs with a batch size of 8 and
without any data augmentations to ensure a fair comparison.

##  4 Experiments

Figure 3: Example images of the industrial burner flames dataset with the
ground truth labels overlaid as transparent red regions. The upper row
displays the original labels by Großkopf et al., (2021), whereas the lower
three images represent the labels that we created.

(a) Input Image

(b) Ground Truth

(c) GTH

(d) RG

(e) SVM

(f) RF

(g) MLP

(h) DL3+ (RN18-I)

Figure 4: Qualitative comparison between the input image (a) overlaid in
transparent red color with the ground truth (b), the segmentation results of
Global Thresholding (c), Region Growing (d), Support Vector Machines (e),
Random Forest (f), Multilayer Perceptron (g), and DeepLabV3+ with a ResNet-18
backbone pre-trained on ImageNet (h).

In this section, we share a variety of experiments conducted on the basis of a
modified public dataset to gather helpful insights about the strengths and
limitations of all the methods in our study.

###  4.1 Dataset

All of our experiments are based on a modified version of the publicly
available industrial burner flames dataset provided by Großkopf et al.,
(2021). The original dataset consists of 3000 labeled grayscale images of two
industial burner flames with 552 $\times$ 552 pixels in size.

In Figure 3, the upper row shows that some labels in the original dataset are
questionable. We suspect that these labels were at least in part created
automatically. To address this, we randomly selected 200 images and relabeled
them by hand, creating a new dataset of 160 training images and 40 test
images. As shown in the lower row of Figure 3, this process resulted in an
improved label quality. In the original labels, flames represented 23.8% of
the dataset, whereas in our labels the portion of flames is 26.5%. Finally, we
computed the Intersection over Union (IoU) between the original labels and our
labels, which yielded a score of 80.8%.

###  4.2 Quantitative Evaluation

| IoU [%] $\uparrow$ | Inference Time [ms] $\downarrow$
---|---|---
GTH | 80.3 | 0.1
RG | 77.0 | 13.4
SVM | 82.3 | 8.2
RF | 87.0 | 124.8
MLP | 86.6 | 106.3
U-Net (MN) | 92.6 (91.9) | 106.7 (7.6)
U-Net (RN18) | 92.9 (92.6) | 138.4 (5.6)
U-Net (RN101) | 93.1 (91.9) | 436.9 (16.6)
DL3+ (MN) | 92.6 (91.9) | 81.2 (6.5)
DL3+ (RN18) | 93.2 (92.9) | 117.8 (4.6)
DL3+ (RN101) | 92.9 (92.3) | 394.3 (15.5)
Table 1: Quantitative comparison between the traditional image processing,
traditional machine learning, and deep learning Intersection over Union (IoU)
and inference time per image. IoU scores in parantheses refer to training from
scratch, whereas the regular scores depict fine-tuning from ImageNet.
Inference times in parantheses were computed on a GPU, while the regular
values are CPU times.

Table 1 presents a quantitative comparison between the best segmentation
results obtained by each method in our study, along with their respective
inference time per image. Unless otherwise noted, we used an AMD EPYC 7502
32-Core CPU with 1 TB of RAM. The results demonstrate that there are clear
improvements going from traditional image processing (GTH: 80.3%) over
traditional machine learning (RF: 87.0%) to deep learning (DL3+ (RN18-I):
93.2%). There is, however, a corresponding increase in CPU time going from 0.1
ms per image with GTH up to 436.9 ms with U-Net (RN101-I).

Traditional Image Processing. Looking at the traditional image processing
techniques in particular, GTH outperforms RG in both the segmentation task and
the needed inference time. GTH yields an IoU score of 80.3% which is 3.3%
higher than what RG achieves. On top of that, GTH only needs 0.1 ms to process
an image, whereas RG needs 13.4 ms.

Traditional Machine Learning. In comparison to the traditional image
processing techniques, the machine learning approaches, SVM, RF, and MLP,
yield a significant performance improvement in the segmentation task. SVM
achieved an IoU score of 82.3%, outperforming both GTH and RF by noteworthy
margin. In addition, SVM only requires 8.2 ms to process an image, making it
faster than RF but slower than GTH. With 87.0% RF achieved the highest IoU
score among the traditional machine learning methods. However, it also
requires 124.8 ms to process an image. MLP achieved a very similar IoU score
of 86.6%, but with a slightly faster inference time of 106.3 ms.

Deep Learning. Judging by the results laid out in Table 1, all of the deep
learning approaches outperform the traditional image processing and machine
learning methods on the segmentation task. Depending on the configuration, the
models achieve IoU scores between 91.9% and 93.2%. Table 1 also shows that
training from scratch in comparison to fine-tuning from ImageNet pre-training
deteriorates performance by 0.3% to 1.2%. As Table 1 displays, deep learning
comes at a high computational cost. Inference times on the CPU range from 81.2
ms for DL3+ (MN) to a maximum of 436.9 ms for U-Net (RN101). However,
inference times can be decreased greatly if inference is performed on a GPU.
In our case, using a common NVIDIA GeForce RTX 3090 GPU with 24 GB of memory
reduced inference times to a range from 4.6 to 16.6 ms depending on the
architecture. Among all of the deep learning models, DL3+ (RN18-I) achieved
the highest IoU score of 93.2% while simultaneously having the fastest
inference time of 4.6 ms with the GPU.

###  4.3 Qualitative Evaluation

Figure 4 shows a visual comparison between the traditional image processing
techniques, the traditional machine learning approaches, and the best deep
learning model. Overall, the qualitative evaluation of the segmentation
results confirms the observations of the quantitative analysis: There are
clear improvements going from traditional image processing to traditional
machine learning to deep learning. Figure 5 in the Appendix shows more
qualitative examples, which corroborate this claim.

Traditional Image Processing. The two traditional image processing techniques,
GTH and RG, largely suffer from the same shortcoming: Both oversegment the
industrial burner chamber and undersegment the flame. As both methods solely
rely on the underlying gray values, there is no way for these methods to
distinguish between bright areas of the image that belong to the chamber or
darker parts of the flame. Hence, the segmentation results are systematically
flawed in these situations.

Traditional Machine Learning. In comparison to the traditional image
processing techniques, the machine learning approaches, SVM, RF, and MLP, do
not suffer from oversegmentation of the chamber. However, they undersegment
darker parts of the flames too, which is especially noticeable for SVM.
Overall, all of the traditional machine learning methods can improve upon the
traditional image processing techniques due to the extra information added by
the hand-crafted features.

Deep Learning. Visually, an even better segmentation result is achieved by the
best deep learning model DL3+ (RN18-I). The segmentation result suffers from
no apparent systematic shortcomings. Through the training process, the deep
learning model learns meaningful features and patterns that seem to generalize
very well to the test data. This also holds for the other deep learning models
not shown in Figure 4.

###  4.4 Impact of Training Dataset Size

| IoU [%] $\uparrow$ | Performance Loss [%] $\downarrow$
---|---|---
GTH | 78.6 | 1.7
RG | 74.3 | 2.7
SVM | 80.6 | 1.7
RF | 84.7 | 2.3
MLP | 84.9 | 1.7
U-Net (MN) | 91.7 (90.5) | 0.9 (1.4)
U-Net (RN18) | 91.6 (91.1) | 1.3 (1.5)
U-Net (RN101) | 92.1 (88.1) | 1.0 (3.8)
DL3+ (MN) | 91.4 (90.6) | 1.2 (1.3)
DL3+ (RN18) | 91.8 (91.6) | 1.4 (1.3)
DL3+ (RN101) | 91.8 (90.7) | 1.1 (1.6)
Table 2: Quantitative comparison between the traditional image processing,
traditional machine learning, and deep learning IoU with just 40 training
images and 160 test images. Additionally, the performance loss compared to the
regular dataset with 160 training images is displayed. The IoU scores in
parantheses refer to training from scratch, whereas the regular scores depict
fine-tuning from ImageNet.

In order to evaluate the effect of the number of training images, we retuned
all of the traditional image processing and traditional machine learning
methods and retrained all deep learning models with an inverse ratio of
training and test images, i.e. we use just 40 randomly selected images for
training and the remaining 160 images for testing.

As Table 2 shows, the observations from the previous experiments remain
unchanged: There are significant segmentation improvements going from
traditional image processing (GTH: 78.6%) to traditional machine learning
(MLP: 84.9%) to deep learning (U-Net (RN101-I): 92.1%). To our surprise,
however, the average performance loss was larger for the traditional image
processing techniques and traditional machine learning approaches.

GTH and RG suffered a performance loss of 1.7% and 2.7%, respectively. The IoU
of the traditional machine learning-based methods deteriorated by 1.9% on
average. Whereas the average performance loss for the deep learning models
pre-trained on ImageNet was only 1.2% and 1.8% for the models trained from
scratch.

##  5 Discussion

We conducted extensive experiments to compare traditional image processing
techniques with traditional machine learning and deep learning methods. In the
following, we discuss our key insights into the strengths and limitations of
each approach.

Traditional Image Processing. GTH and RG are both effective for creating a
baseline binary segmentation of industrial burner flames. They are
computationally efficient, require little tuning and offer fast inference
times, making them a good choice when speed is a priority. In our experiments,
GTH outperformed RG on the segmentation task, with the fastest inference time
of all examined methods with just 0.1 ms per image. However, these techniques
may not be suitable if the quality of the segmentation results is more
important, as they rely solely on gray values and can suffer from under- and
oversegmentation.

Traditional Machine Learning. SVM, RF, and MLP significantly improve
segmentation results compared to GTH by using hand-crafted features. For
example, RF achieved a 6.7% relative IoU score improvement over GTH. However,
these methods are computationally more expensive and require careful
engineering and feature selection. It is worth noting that adding more
features could improve the performance. On the other hand, it can also
introduce noise and lead to overfitting, aside from increasing computation
time. As a possible solution, SVM and RF allow the computation of feature
importances to help select appropriate features.

Deep Learning. Our experiments show that the deep learning models U-Net and
DL3+ achieve the best segmentation performance overall with a maximum IoU
score of 93.2%. Surprisingly, the choice of architecture or pre-training on
ImageNet had very little impact on the performance, although the latter helped
in all experiments. Another revealing advantage of deep learning is its
ability to generalize from small datasets, as the models suffered the smallest
performance loss when trained on less data. The only apparent downside to the
deep learning methods is their high computational cost. However, training and
inference times can greatly benefit from using a GPU. Overall, deep learning
is the best choice for binary segmentation of industrial burner flames,
particularly if a GPU is available.

##  6 Conclusion

In this work, we conducted a comparative study of traditional image processing
techniques, traditional machine learning methods, and deep learning approaches
for the binary segmentation of industrial burner flames. Extensive
experimentation on a modified version of a public dataset (Großkopf et al.,,
2021) revealed key insights into the strengths and limitations of each
approach.

Traditional image processing techniques like Global Thresholding and Region
Growing offer a fast and simple solution but suffer from systematic under- and
oversegmentation. Traditional Machine learning methods, such as Support Vector
Machines, Random Forests, and Multilayer Perceptrons, improve the segmentation
performance in exchange for increased computational cost and feature
engineering effort. Deep learning models, like U-Net and DeepLabV3+, achieve
the best segmentation performance in our study while showing remarkable
ability to generalize even from small datasets. Despite their high
computational requirements, they are the best choice for binary segmentation
of industrial burner flames, especially if a GPU is available.

In summary, our study provides 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. With the
continuing progress in deep learning and the increasing availability of
compute power, we expect these methods to become even more capable and
efficient in the future.

## Acknowledgment

This research is funded by Invest BW on behalf of the Ministry of Economics,
Labor and Tourism of Baden-Württemberg.

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## APPENDIX

Figure 5: More qualitative comparisons between the input image in the first
row, the ground truth in the second row, the segmentation results of GTH in
the third row, RF in the fourth row, and DL3+ (RN18-I) in the last row.