# Color Recognition in Challenging Lighting Environments: CNN Approach
††thanks: Identify applicable funding agency here. If none, delete this.

1st Nizamuddin Maitlo∗ Department of Computer Science
Sukkur IBA University
Sukkur, Pakistan
Corresponding Author<EMAIL_ADDRESS>2nd Nooruddin Noonari
Department of Computer Science
IBA Community College, Naushahro Feroze
Sindh, Pakistan
Corresponding Author<EMAIL_ADDRESS>3rd Sajid Ahmed Ghanghro
Depeartment of Computer Science
Saitama University, Japan
S.A.LU, Khairpur, Sindh, Pakistan
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>4th Sathishkumar Duraisamy  Department of
Mechanical Engineering
Kathir College of Engineering
Tamilnadu, India.
<EMAIL_ADDRESS>5th Fayaz Ahmed  Institute of Computer Science
S.A.LU, Khairpur
Sindh, Pakistan
<EMAIL_ADDRESS>
###### Abstract

Light plays a vital role in vision either human or machine vision, the
perceived color is always based on the lighting conditions of the
surroundings. Researchers are working to enhance the color detection
techniques for the application of computer vision. They have implemented
proposed several methods using different color detection approaches but still,
there is a gap that can be filled.

To address this issue, a color detection method, which is based on a
Convolutional Neural Network (CNN), is proposed. Firstly, image segmentation
is performed using the edge detection segmentation technique to specify the
object and then the segmented object is fed to the Convolutional Neural
Network trained to detect the color of an object in different lighting
conditions. It is experimentally verified that our method can substantially
enhance the robustness of color detection in different lighting conditions,
and our method performed better results than existing methods.

###### Index Terms:

Deep Learning, Convolutional Neural Network (CNN), Image Segmentation, Color
Detection, Object Segmentation.

##  I Introduction

Recently researchers have been working on different color detection techniques
to enhance the computer’s vision applications to be more accurate and precise.
In computer vision, color plays a very important role in different techniques
such as object detection, object tracking, image retrieval, object
recognition, and so on. While comparing color images with gray-scale images
color images provide more information than gray-scale images. [1].

Several techniques have been implemented by researchers by using color
detection such as [2] a Convolutional Neural Network is implemented to detect
vehicle color. Further, they mentioned that the CNNs are naturally designed to
perform classification based on the shape of the vehicle but their research
proved that CNNs can be trained to perform color classification and can
perform very well. In their method they converted the input image into
different two color spaces, HSV and CIE Lab, and performed them to some CNN
architecture. In this paper [3], researchers explored an object detection
technique based on fusion architecture. Two automotive datasets namely the
KAIST multispectral pedestrian dataset and FLIR thermal object detection
dataset are explored for both these sensors. In their research, they found
that both models are best in some aspects and outperformed in different
conditions. According to their complementary nature, the Color model
outperforms Thermal in day conditions and the Thermal model outperforms color
in night conditions. They have used CNN fusion architecture of mid-level that
performed significantly better than the existing baseline models. They have
observed 0.6% improvements in experimental results compared to existing
baseline methods. In [4], researchers investigated the impact of basic image
processing and contrast enhancement techniques such as CLAHE and MSRCR on CNN-
based skin cancer detection. The findings indicate that, in comparison to
MSRCR, CLAHE is preferable for color image enhancement in early skin cancer
detection using CNN. Nevertheless, both the original and CLAHE-enhanced
datasets demonstrated equivalent accuracy during training and validation. The
primary finding of this study suggests that image contrast enhancement is
unnecessary for effective skin cancer screening. In this study [5],
researchers have introduced a light field imaging technique designed to
address challenges in underwater imaging under low-light conditions. Their
approach focuses on mitigating the issue of light scattering in light field
images through the utilization of deep convolutional neural networks
incorporating depth estimation. Additionally, They employ a color correction
method based on spectral characteristics to restore diminished colors. The
experimental outcomes demonstrate the efficacy of their proposed method in
addressing real-world challenges associated with underwater imaging.

To address the issue of color detection, numerous existing methods have
demonstrated the capability to accurately identify a wide range of colors.
Nonetheless, this task remains challenging due to the influence of
environmental factors. Achieving precise color recognition is crucial for
applications involving visual inspection. One well-known approach for color
detection is the utilization of the HSV (Hue, Saturation, and Value) color
space, as documented in previous works [6][7]. The effectiveness of this
method greatly depends on the accurate determination of the minimum and
maximum range for a specific color, a process often guided by statistical
techniques. Failure to do so can result in the susceptibility of the specified
color range to environmental variations, including changes in lighting
conditions. HSV color space is particularly robust when it comes to detecting
a specific color with precision. In such cases, the range can be carefully
calculated to ensure accurate color detection. However, the HSV color
detection method may not be universally applicable, especially in scenarios
where the objective is to detect multiple colors, and some of these colors
exhibit similar properties. Therefore, there is a pressing need for the
development of innovative methods that can emulate the human visual system’s
ability to accurately perceive colors. This is essential because color
perception can be significantly influenced by various sources of illumination,
such as natural sunlight, LED lighting, and CCFL (Cold Cathode Fluorescent
Lamp) lighting.

In response to the aforementioned challenge, we introduce a novel color
detection approach that leverages the power of Convolutional Neural Networks
(CNNs). Our method combines image segmentation with edge detection techniques
to accurately delineate the object of interest. Subsequently, this segmented
object is input into a trained Convolutional Neural Network, specifically
designed to excel in detecting object colors across varying lighting
conditions. Experimental validation demonstrates the remarkable effectiveness
of our approach in enhancing color detection robustness under diverse lighting
scenarios, surpassing the performance of established methods.

The subsequent sections of this article are structured as follows:

  * •

Literature Review (Section II): This section provides a concise overview of
the relevant literature in the field.

  * •

Proposed Methodology for the Indoor Navigation System of Autonomous Vehicles
(Section III): Section III offers a brief description of the methodology
proposed for the indoor navigation system of autonomous vehicles.

  * •

Results and Discussion (Section IV): In this section, you will find the
results and a detailed discussion of the outcomes obtained through the
application of the proposed methods.

  * •

Conclusion (Section V): The article concludes in this section, summarizing the
key findings and contributions of the study.

This organization provides a clear structure for readers to understand the
flow of the article and easily locate specific information within it.

##  II Color Spectrum System

Figure 1: Spectrum of six different colors, whose brightness changes from dark
to light.

In Figure 1 spectrum of different colors, whose brightness changes from dark
to light is shown in the image. Our algorithm is focused on this change in the
lighting of color and will enhance the color detection capability of the
applications.

##  III Literature Review

Currently, researchers are exploring various ways to improve the effectiveness
and efficiency of color detection by proposing new techniques, specifically in
different lighting conditions. Color is very useful in several applications
such as object detection, edge detection, and so on but color detection
through baseline techniques is still limited. These methods are as follows.

This study [8] focuses on developing a high-performance traffic sign detection
system using deep learning techniques. The study delves into two key aspects
of the recognition system: the color space used to process the input video and
the structure of the deep learning network. The study combines three different
types of network models based on faster R-CNN and R-FCN architectures and
thoroughly evaluates six different color spaces, namely RGB, Normalized RGB,
Ruta’s RYG, YCbCr, HSV, and CIE Lab. The experiment consists of end-to-end
testing on a traffic light dataset with a resolution of 1280×720. Notably,
simulations show that the best performance is achieved when the RGB color
space and the Faster R-CNN model are used together.

This paper [9] uses fine-grained convolutional neural networks (CNNs) to
accurately predict scene lighting, avoiding the use of hand-crafted features
prevalent in previous methods. CNNs operate exclusively in the spatial domain
and take image patches as input, and their architecture consists of one
convolutional layer (including max pooling), a fully connected layer, and
three dedicated output nodes. It is worth noting that this network
architecture seamlessly integrates feature learning and regression into a
single optimization process, thus creating a more effective model for scene
illumination estimation. This approach demonstrates excellent performance and
sets a new standard for standard RAW image datasets. Experimental results
highlight the effectiveness of the proposed CNN in estimating scene
illumination. Notably, preliminary experiments on images with spatially
varying illumination characteristics confirmed the robustness and stability of
the local illumination estimation capabilities of the CNN.

This paper [10] proposes a new low-light image enhancement model called MSR-
net, which combines the principles of Retinex theory and convolutional neural
networks (CNNs). By establishing an equivalence between multiscale Retinex and
feedforward CNN, the model directly learns the global mapping between dark and
light images, reformulating low-light image enhancement as a machine-learning
problem. Compared with traditional methods, most MSR network parameters are
optimized through error backpropagation, eliminating the dependence on manual
parameter tuning. Experimental evaluation of complex images shows that the
model qualitatively and quantitatively outperforms state-of-the-art methods,
highlighting its potential to advance the field of low-light image
enhancement.

This study [11] developed a robust crack detection method by implementing
transfer learning as an alternative to training original neural networks. The
study explores three deep learning approaches: 1) building an accurate
convolutional neural network from scratch, 2) using the output functions of
the VGG16 network architecture pre-trained on the ImageNet dataset, and 3)
fine-tuning the top layer of VGG16. To mitigate problems associated with
limited and imbalanced training datasets, data augmentation is used. The
dataset contains fatigue test photographs and inspection images taken under
different conditions such as uncontrolled distance, illumination, angle and
blur. It is worth noting that the increase in data significantly helped
improve the accuracy index, which increased the performance of the three
verification methods by 5%, 2%, and 5%, respectively. This comprehensive
approach to crack detection highlights the effectiveness of transfer learning
in addressing real-world variations and limitations in training datasets.

This paper [12] proposes a convolutional neural network (CNN)-based model that
enables end-to-end real-time lighting estimation in mixed reality environments
even without a priori scene information. Ambient lighting is represented using
spherical harmonics (SH), specifically designed to effectively capture area
lighting. The proposed CNN architecture takes RGB images as input and quickly
recognizes ambient lighting. This study differs from traditional CNN-based
illumination estimation methods, which require a highly optimized deep neural
network architecture with a small number of parameters. This design enables
the model to skillfully learn complex lighting scenarios from realistic high
dynamic range (HDR) environment images, highlighting its potential for robust
and efficient lighting estimation in a variety of mixed reality environments.

The study [13] presents a system designed for face recognition in low-light
conditions, using a dual strategy of low-light image enhancement and face
detection. Another study [14] focused on precise modeling of object color
recognition thresholds under different lighting conditions. In addition, a
separate study proposed a new data-driven model [15] specifically designed to
estimate high dynamic range (HDR) lighting conditions from a single low
dynamic range (LDR) spherical panoramic image. Notably, the inherently complex
and flawed task of illumination estimation is further complicated by the lack
of reliable illumination data, which poses a major challenge for the practical
application of data-driven methods. Subsequent research has investigated
object detection in complex environments. One study [16] calls these
situations complex environments and points to advances in deep learning-based
object detection. Another study [17] pioneered the use of high dynamic range
(HDR) imaging in object detection, increasing the ability to capture and
process a wide dynamic range of a scene, similar to the human eye. Other
studies [18],[19] improve low-light images by implementing learning
techniques, with applications ranging from light-field image enhancement to
low-light image enhancement. Finally, another paper [20] shifts the focus to
using image enhancement techniques to improve object detection performance
rather than improving the quality of human sensory perception.

##  IV Methodology

The concept of employing color spectrums for training neural networks drew
inspiration from nature. In numerous instances, a color’s appearance can be
noticeably influenced by environmental conditions, causing subtle shifts in
its visual perception. For instance, a transition from light yellow to a
slightly darker shade of yellow might still be perceived as yellow by the
human eye. However, in the context of machine vision, this issue becomes
considerably more consequential. Accurate color measurement becomes imperative
in certain applications to effectively differentiate between two similar
objects based on their color characteristics.

###  IV-A Dataset

The proposed CNN is trained using a dataset of 250 images of different
variations of colors in different lighting conditions. The dataset is divided
into training and testing for training and testing purposes, we have used 200
images for training and the remaining 50 for the testing purpose of the model.

###  IV-B Experimental Settings

To perform the experiments we have used the system with CPU: Intel Core i7
8700K; memory: 16 GB RAM; OS: Ubuntu 16.04 for training and testing the
proposed framework with different lighting conditions.

###  IV-C CNN

We have proposed a Convolutional Neural Network (CNN) architecture for color
classification containing three convolutional layers, along with Rectified
Linear Unit (ReLU) activation, augmenting feature extraction, and infusing
non-linearity. The first three convolutional layers are conjoined with Max
Pooling operations to effectuate spatial dimension reduction. Subsequent to
this convolutional sequence, three fully connected layers perform feature
abstraction, ultimately feeding into an output layer tailored for color
categorization.

Figure 2: Spectrum of six different colors, whose brightness changes from dark
to light.

A typical CNN layer can be represented with the following equations:

$z_{i}=(w*x)_{i}+b_{i}$ (1)

$a_{i}=f(z_{i})$ (2)

Here, $z_{i}$ is the weighted sum of the input activations, $w$ is the weight
matrix, $x$ is the input feature map, $b_{i}$ is the bias term, and $f(\cdot)$
is the activation function.

The convolution operation in equation (1) is defined as:

$(w*x)_{i}=\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}w_{m,n}\cdot x_{i+m,n}$ (3)

where $w_{m,n}$ is the weight at position $(m,n)$ in the filter, and
$x_{i+m,n}$ is the input at position $(i+m,n)$.

The activation function $f(z_{i})$ can be a non-linear function such as the
Rectified Linear Unit (ReLU):

$f(z_{i})=\max(0,z_{i})$ (4)

####  IV-C1 Pooling Layer

A pooling layer can be represented as follows:

$y_{i,j}=\max_{m=0}^{K-1}\max_{n=0}^{K-1}x_{i\cdot K+m,j\cdot K+n}$ (5)

Here, $y_{i,j}$ is the output of the pooling layer, and $K$ is the pooling
size.

###  IV-D Proposed Framework

The concept of employing color spectrums in training neural networks draws
inspiration from nature. In various instances, environmental conditions can
notably influence a color, altering its visual appearance. Consider, for
instance, the transition from light yellow to a slightly darker shade of
yellow; to the human eye, it remains categorized as yellow. While this might
not be a significant concern in human vision, it holds crucial importance in
machine vision. Achieving precise color measurements becomes imperative in
certain applications where distinguishing between two similar objects relies
on their color properties.

We have proposed a framework in which the first image is fed to the region
proposal module which detects the object using Sobel edge detection and then
edges are linked to form the object. Further, the detected objects are merged
and sent to CNN for color detection to the CNN Training and classification
module in which first the model is trained using a color dataset and then the
model classifies the color based on the training.

Figure 3: Spectrum of six different colors, whose brightness changes from dark
to light.

###  IV-E Bounding Box Overview

A bounding box is a rectangular frame that encapsulates and precisely outlines
the spatial extent of an object or region within an image. Widely used in
computer vision tasks, particularly object detection, bounding boxes provide a
standardized representation, specifying the object’s location and facilitating
subsequent analyses. These boxes are defined by their coordinates, typically
represented by the top-left and bottom-right corners, enabling efficient
localization and recognition of objects in image datasets.

Our approach entails a systematic process of object detection, wherein
bounding boxes are employed as integral annotations. Before the subsequent
identification of color attributes within the designated regions or objects,
the establishment of bounding boxes is imperative. This preliminary step in
the computational framework serves to precisely localize and delineate objects
of interest within the visual domain. By employing this methodological
sequence, we aim to enhance the accuracy and specificity of color
identification processes, thus contributing to the robustness and efficacy of
our overarching scientific inquiry in computer vision applications.

###  IV-F Pusodocode

The provided pseudocode outlines a method for extracting color cubes from a
given bounding box in an RGB image. The nested loops iterate over a 3x3 grid
within the bounding box, determining the center coordinates for each color
cube. Subsequently, a sequence of color cubes is extracted from the bounding
box image based on a specified size. These color cubes are then input into a
neural network model, denoted as RCCNet, for predictive analysis. The
algorithm facilitates the systematic processing of localized regions within
the bounding box, enabling the recognition or extraction of features relevant
to the specified task, such as color identification.

Input : $x,y,w,h$ // Bounding box information by MBR, $img$ // original image
RGB, $size=32$ // Size for each cube to recognize

Output : $rectCubeImg$ // sequence of color cubes,
$boundBoxImg=img(Range(x,x+w),Range(y,y+h))$

1

2for  _$i=0$ to $2$_ do

3 $x1=\frac{w}{3}\cdot\frac{1}{2}+i\cdot 3$;

4 for  _$j=0$ to $2$_ do

5 $y1=\frac{h}{3}\cdot\frac{1}{2}+j\cdot y$;

6
$rectCubeImg=boundBoxImg(Range(x1-\frac{size}{2},x1+\frac{size}{2}),Range(y1-\frac{size}{2},y1+\frac{size}{2}))$;

7 RCCNet.Predict($rectCubeImg$);

8

9

Algorithm 1 Process Bounding Box

##  V Experiment and Results

Figure 4: Spectrum of six different colors, whose brightness changes from dark
to light.

The graph denoted as Figure 5 illustrates the trajectory of training and
validation loss. Notably, the observed pattern indicates a linear decrease in
both training and validation loss as the number of epochs progresses.

Figure 5: Training loss and Validation Loss

Displayed in Figure 6 are the training and validation accuracy curves. This
representation reveals that the model’s accuracy experiences an initial ascent
from the first epoch, followed by a marginal decline between epochs 100 and
200. Subsequently, beyond the 200th epoch, the accuracy graphs demonstrate a
linear upward trend.

Figure 6: Training Accuracy and Validation Accuracy

Similarly, the graph referenced as Figure 7 delineates the training and
testing accuracy. Analogous to the pattern observed in Figure 6, the accuracy
initially rises from the onset and undergoes a minor dip between epochs 100
and 200, only to resume a linear increase thereafter.

Figure 7: Training Accuracy and Testing Accuracy

The graph in figure Figure 8 shows that our proposed Convolutional Neural
Network (CNN) surpasses Support Vector Machines (SVM), Long Short-Term Memory
(LSTM), and Random Forest (RF) in performance. The CNN’s ability to
automatically learn intricate features from raw data, capture spatial
hierarchies in image data, and engage in end-to-end learning distinguishes it,
resulting in superior outcomes compared to traditional models.

Figure 8: Model Comparison with other approaches over different No of Images

##  VI Our Results

The image capture process is initiated using an imaging device, followed by
preprocessing involving Gaussian Blur. Subsequent to this, the Adaptive
Threshold method surpasses the standard threshold function in effectively
segmenting the bounding box, as depicted in Figure 9. Employing the contour
method on the output of the adaptive threshold facilitates the detection of
the bounding box, with further processing of identified contours to ascertain
the bounding box’s edges. Remarkably, the largest contour in the image is
designated as the bounding box, highlighted by the outer red pixels in Figure
9. The Minimum Bounding Rectangle (MBR) method is then applied to extract
crucial details such as position, width, and height for subsequent cropping
from the input RGB image. Following the acquisition of bounding box specifics
via the MBR method, a feature extraction procedure is initiated to isolate the
bounding box from the RGB image. Consequently, color features are extracted
from the bounding box to facilitate recognition. The results of the bounding
box detection and feature extraction are visually represented in Figure 9.
Ultimately, our Convolutional Neural Network (CNN) is employed for the
recognition of each color within the detected bounding box. Significantly,
CNN’s training encompasses variations in illuminations, ensuring accurate
color recognition based on distinctive spectral characteristics.

Figure 9: Our Purposed Method Results

##  VII Discussion

In response to the challenge of robust color detection in diverse lighting
conditions, we propose a Convolutional Neural Network (CNN)-based method.
Initially, image segmentation is conducted using edge detection segmentation
to precisely identify the object. Subsequently, the segmented object is input
into a CNN trained for color detection under varying lighting conditions.
Experimental validation demonstrates that our approach significantly improves
the robustness of color detection across diverse lighting scenarios,
surpassing the performance of existing methods.

Our study underscores the superior performance of our proposed CNN in contrast
to Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Random
Forest (RF). The CNN’s inherent capacity to autonomously extract intricate
features from raw data, capture spatial hierarchies within image data, and
facilitate end-to-end learning sets it apart, yielding outcomes superior to
traditional models. This research contributes valuable insights into the
efficacy of CNNs for enhanced color detection, particularly in challenging
environmental conditions.

##  VIII Conclusion

Light plays a crucial role in both human and machine vision, where the
perceived color is inherently influenced by the ambient lighting conditions.
To advance color detection techniques for computer vision applications,
researchers have explored various methods using distinct color detection
approaches. However, despite these efforts, a discernible gap remains in the
existing literature.

In response to this gap, we propose a color detection methodology based on a
Convolutional Neural Network (CNN). Initially, image segmentation is conducted
using an edge detection segmentation technique to delineate the object of
interest. Subsequently, the segmented object is input into a Convolutional
Neural Network that has been trained to identify the color of objects under
diverse lighting conditions. Through experimental validation, our method
demonstrates a notable improvement in the robustness of color detection across
varying lighting conditions. Our approach outperforms existing methods,
affirming its efficacy in addressing the challenges associated with color
detection in diverse lighting environments.

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