2D image edge detection enhancement using convolutional neural network

Gaoming Lin

doi:10.54254/2755-2721/2/20220519

Research Article

Open access

Published on 22 March 2023

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Lin,G. (2023). 2D image edge detection enhancement using convolutional neural network. Applied and Computational Engineering,2,1-8.

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2D image edge detection enhancement using convolutional neural network

Gaoming Lin *^,1,

¹ University of Florida, Gainesville FL 32611, USA

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2/20220519

Abstract

Traditional edge detection operators are usually applied on edge detection in 2D image processing. However, the edge detection system equipped with simple operators has many disadvantages, such as high sensitivity to noise and neglect of significant edge details. This work proposed a method to enhance edge detection with convolutional neural net-work. To overcome the shortcomings of the system using simple edge detection operators in 2D image processing, an edge detection system using convolutional neural network was developed with Python language. In the convolutional neural network, two convolutional layers were designed to extract 2D image features that were relative to edge information. Then a normalization layer was applied to normalize the convoluted output. After that, pre-processing was utilized to denoise and smooth the image input. The final step was edge detection using traditional operators. Experiments were also implemented to verify the improvement of the plugin of the three-layer convolutional neural network in the de-signed edge detection system. Relative frequencies were utilized to quantify the edge de-tection performance. Results showed that the involvement of convolutional neural net-work could strengthen edge detection operators’ performance obviously in computer vi-sion.

Keywords

edge detection, convolutional neural network, image processing, computer vision

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Cite this article

Lin,G. (2023). 2D image edge detection enhancement using convolutional neural network. Applied and Computational Engineering,2,1-8.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

Conference website: https://www.confcds.org/

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)

Conference date: 16 July 2022

Editor：Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.2

ISSN：2755-2721(Print) / 2755-273X(Online)

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