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The Application of Image Moments and Gaussian Blur for Line Detection in Differential-Drive Mobile Robots

by Mohammad Shamoushaki, Mohammad Hasan Ghasemi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 53
Year of Publication: 2024
Authors: Mohammad Shamoushaki, Mohammad Hasan Ghasemi
10.5120/ijca2024924218

Mohammad Shamoushaki, Mohammad Hasan Ghasemi . The Application of Image Moments and Gaussian Blur for Line Detection in Differential-Drive Mobile Robots. International Journal of Computer Applications. 186, 53 ( Dec 2024), 43-51. DOI=10.5120/ijca2024924218

@article{ 10.5120/ijca2024924218,
author = { Mohammad Shamoushaki, Mohammad Hasan Ghasemi },
title = { The Application of Image Moments and Gaussian Blur for Line Detection in Differential-Drive Mobile Robots },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2024 },
volume = { 186 },
number = { 53 },
month = { Dec },
year = { 2024 },
issn = { 0975-8887 },
pages = { 43-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number53/the-application-of-image-moments-and-gaussian-blur-for-line-detection-in-differential-drive-mobile-robots/ },
doi = { 10.5120/ijca2024924218 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-12-07T02:20:23.470533+05:30
%A Mohammad Shamoushaki
%A Mohammad Hasan Ghasemi
%T The Application of Image Moments and Gaussian Blur for Line Detection in Differential-Drive Mobile Robots
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 53
%P 43-51
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Over the last several years, a multitude of research studies have been carried out in the field of artificial intelligence to set up autonomous systems. Various research papers have used machine learning methods to enhance the efficiency of robots. Utilizing the information captured by the camera or evaluating the data acquired by diverse sensors in systems will also optimize their performance. The upcoming research investigates the programming of a differential-drive mobile robot (DDMR) capable of following environmental features in a laboratory. The robot processes RGB images using image moments and a Gaussian filter. Furthermore, the robot was simulated in ROS to verify the effectiveness of the image processing method. Obstacle avoidance was performed using ultrasonic sensors and the Bubble Rebound method. Furthermore, image moments assist in detecting the center points of guidelines and subsequently enable maintaining or changing lanes as required. A model has been trained to detect environmental signals using the Haar Cascade Classifier. The primary purpose of this paper is to study the application of one of the fundamental image features, called image moments, in lane detection, along with masking and feature-based image processing. Additionally, the effect of different sizes of kernel matrix in the Gaussian filter for noise reduction has been compared. The robot was eventually tested in a laboratory environment to validate the method and scenario.

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Index Terms

Computer Science
Information Sciences

Keywords

Differential-Drive Mobile Robot The Bubble Rebound Algorithm Lane Detection Haar Cascade Classifier Image Moments Gaussian Blur

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