Object Identification Using Manipulated Edge Detection Techniques

Muhammad Yasir; Md. Sakaouth Hossain; Shah Nazir; Sulaiman Khan; Rahul Thapa

doi:doi:10.11648/j.scidev.20220301.11

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Object Identification Using Manipulated Edge Detection Techniques

Muhammad Yasir, Md. Sakaouth Hossain, Shah Nazir, Sulaiman Khan, Rahul Thapa

Published in Science Development (Volume 3, Issue 1)

Received: 15 December 2021 Accepted: 4 January 2022 Published: 12 January 2022

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Abstract

Object detection is a computer vision technique for locating instances of objects in images. Because edge detection techniques are at the forefront of image processing for object recognition, having a thorough grasp of them is essential. Object detection techniques play a significant rule in image processing. The proposed study aims to present different edge detection techniques that include prewitt, Robert, Sobel and canny edge detection techniques. The purpose of this study is to examine several edge detection methods, such as Canny, Sobel, Prewitt, and Roberts, in order to determine the accurate boundary of an object. The research is based on a collection of aeroplane images obtained from the Aeroplane Design and Engineering Database. Based on the analysis and results, it was observed that canny outperforms then other techniques in properly detecting the object, with an accuracy of 98.84%, compared to Sobel 91.75%, Prewitt 83.74%, and Robert 79.45%. When compared to the Sobel edge detection algorithm, prewitt edge detection algorithm, Robert edge detection algorithm, it has been shown that the canny edge detection algorithm delivers superior accuracy in edge detection and execution time. The proposed model's applicability is compared to various generic edge recognition methods such as Sobel, Prewitt, and Robert edge detection approaches, with the conclusion that our model outperforms others in reliably recognizing aeroplane edges in the image.

Published in	Science Development (Volume 3, Issue 1)
DOI	10.11648/j.scidev.20220301.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Edge Detection, Object Recognition, Aeroplane Imagery, Matlab

References

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APA Style

Muhammad Yasir, Md. Sakaouth Hossain, Shah Nazir, Sulaiman Khan, Rahul Thapa. (2022). Object Identification Using Manipulated Edge Detection Techniques. Science Development, 3(1), 1-6. https://doi.org/10.11648/j.scidev.20220301.11

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ACS Style

Muhammad Yasir; Md. Sakaouth Hossain; Shah Nazir; Sulaiman Khan; Rahul Thapa. Object Identification Using Manipulated Edge Detection Techniques. Sci. Dev. 2022, 3(1), 1-6. doi: 10.11648/j.scidev.20220301.11

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AMA Style

Muhammad Yasir, Md. Sakaouth Hossain, Shah Nazir, Sulaiman Khan, Rahul Thapa. Object Identification Using Manipulated Edge Detection Techniques. Sci Dev. 2022;3(1):1-6. doi: 10.11648/j.scidev.20220301.11

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@article{10.11648/j.scidev.20220301.11,
  author = {Muhammad Yasir and Md. Sakaouth Hossain and Shah Nazir and Sulaiman Khan and Rahul Thapa},
  title = {Object Identification Using Manipulated Edge Detection Techniques},
  journal = {Science Development},
  volume = {3},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.scidev.20220301.11},
  url = {https://doi.org/10.11648/j.scidev.20220301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.scidev.20220301.11},
  abstract = {Object detection is a computer vision technique for locating instances of objects in images. Because edge detection techniques are at the forefront of image processing for object recognition, having a thorough grasp of them is essential. Object detection techniques play a significant rule in image processing. The proposed study aims to present different edge detection techniques that include prewitt, Robert, Sobel and canny edge detection techniques. The purpose of this study is to examine several edge detection methods, such as Canny, Sobel, Prewitt, and Roberts, in order to determine the accurate boundary of an object. The research is based on a collection of aeroplane images obtained from the Aeroplane Design and Engineering Database. Based on the analysis and results, it was observed that canny outperforms then other techniques in properly detecting the object, with an accuracy of 98.84%, compared to Sobel 91.75%, Prewitt 83.74%, and Robert 79.45%. When compared to the Sobel edge detection algorithm, prewitt edge detection algorithm, Robert edge detection algorithm, it has been shown that the canny edge detection algorithm delivers superior accuracy in edge detection and execution time. The proposed model's applicability is compared to various generic edge recognition methods such as Sobel, Prewitt, and Robert edge detection approaches, with the conclusion that our model outperforms others in reliably recognizing aeroplane edges in the image.},
 year = {2022}
}

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TY - JOUR
T1 - Object Identification Using Manipulated Edge Detection Techniques
AU - Muhammad Yasir
AU - Md. Sakaouth Hossain
AU - Shah Nazir
AU - Sulaiman Khan
AU - Rahul Thapa
Y1 - 2022/01/12
PY - 2022
N1 - https://doi.org/10.11648/j.scidev.20220301.11
DO - 10.11648/j.scidev.20220301.11
T2 - Science Development
JF - Science Development
JO - Science Development
SP - 1
EP - 6
PB - Science Publishing Group
SN - 2994-7154
UR - https://doi.org/10.11648/j.scidev.20220301.11
AB - Object detection is a computer vision technique for locating instances of objects in images. Because edge detection techniques are at the forefront of image processing for object recognition, having a thorough grasp of them is essential. Object detection techniques play a significant rule in image processing. The proposed study aims to present different edge detection techniques that include prewitt, Robert, Sobel and canny edge detection techniques. The purpose of this study is to examine several edge detection methods, such as Canny, Sobel, Prewitt, and Roberts, in order to determine the accurate boundary of an object. The research is based on a collection of aeroplane images obtained from the Aeroplane Design and Engineering Database. Based on the analysis and results, it was observed that canny outperforms then other techniques in properly detecting the object, with an accuracy of 98.84%, compared to Sobel 91.75%, Prewitt 83.74%, and Robert 79.45%. When compared to the Sobel edge detection algorithm, prewitt edge detection algorithm, Robert edge detection algorithm, it has been shown that the canny edge detection algorithm delivers superior accuracy in edge detection and execution time. The proposed model's applicability is compared to various generic edge recognition methods such as Sobel, Prewitt, and Robert edge detection approaches, with the conclusion that our model outperforms others in reliably recognizing aeroplane edges in the image.
VL - 3
IS - 1
ER -

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Author Information

Muhammad Yasir

College of Oceanography and Space Informatics, China University of Petroleum, Qingdao, China
Md. Sakaouth Hossain

Department of Geological Sciences, Jahangirnagar University, Dhaka, Bangladesh
Shah Nazir

Department of Computer Science, University of Swabi, Swabi, Pakistan
Sulaiman Khan

Department of Computer Science, University of Swabi, Swabi, Pakistan
Rahul Thapa

Department of Geography, D.B.S (P.G) College, Dehradun, India

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APA Style

Muhammad Yasir, Md. Sakaouth Hossain, Shah Nazir, Sulaiman Khan, Rahul Thapa. (2022). Object Identification Using Manipulated Edge Detection Techniques. Science Development, 3(1), 1-6. https://doi.org/10.11648/j.scidev.20220301.11

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ACS Style

Muhammad Yasir; Md. Sakaouth Hossain; Shah Nazir; Sulaiman Khan; Rahul Thapa. Object Identification Using Manipulated Edge Detection Techniques. Sci. Dev. 2022, 3(1), 1-6. doi: 10.11648/j.scidev.20220301.11

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AMA Style

Muhammad Yasir, Md. Sakaouth Hossain, Shah Nazir, Sulaiman Khan, Rahul Thapa. Object Identification Using Manipulated Edge Detection Techniques. Sci Dev. 2022;3(1):1-6. doi: 10.11648/j.scidev.20220301.11

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@article{10.11648/j.scidev.20220301.11,
  author = {Muhammad Yasir and Md. Sakaouth Hossain and Shah Nazir and Sulaiman Khan and Rahul Thapa},
  title = {Object Identification Using Manipulated Edge Detection Techniques},
  journal = {Science Development},
  volume = {3},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.scidev.20220301.11},
  url = {https://doi.org/10.11648/j.scidev.20220301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.scidev.20220301.11},
  abstract = {Object detection is a computer vision technique for locating instances of objects in images. Because edge detection techniques are at the forefront of image processing for object recognition, having a thorough grasp of them is essential. Object detection techniques play a significant rule in image processing. The proposed study aims to present different edge detection techniques that include prewitt, Robert, Sobel and canny edge detection techniques. The purpose of this study is to examine several edge detection methods, such as Canny, Sobel, Prewitt, and Roberts, in order to determine the accurate boundary of an object. The research is based on a collection of aeroplane images obtained from the Aeroplane Design and Engineering Database. Based on the analysis and results, it was observed that canny outperforms then other techniques in properly detecting the object, with an accuracy of 98.84%, compared to Sobel 91.75%, Prewitt 83.74%, and Robert 79.45%. When compared to the Sobel edge detection algorithm, prewitt edge detection algorithm, Robert edge detection algorithm, it has been shown that the canny edge detection algorithm delivers superior accuracy in edge detection and execution time. The proposed model's applicability is compared to various generic edge recognition methods such as Sobel, Prewitt, and Robert edge detection approaches, with the conclusion that our model outperforms others in reliably recognizing aeroplane edges in the image.},
 year = {2022}
}

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