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 |
Edge Detection, Object Recognition, Aeroplane Imagery, Matlab
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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
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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Download@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}
}
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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