Journal of Applied Science and Engineering

Published by Tamkang University Press

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Automatic extraction algorithm for visual communication design elements based on deep learning

 2026

 Volume 35

Yinjie Zhao

School of Art Education, Hubei Institute of Fine Arts, Wuhan, Hubei 430205, China

Received: March 25, 2026
Accepted: May 27, 2026
Publication Date: August 26, 2026

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Experimental design ideas 

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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This paper addresses the challenge that traditional visual communication design algorithms struggle to extract reliable features from complex design elements. To address this issue, an automatic extraction algorithm based on deep learning is proposed, improving the intelligence and efficiency of visual communication design. Furthermore, the YOLOv7 target recognition model is enhanced by integrating a Squeeze-and-Excitation (SE) attention mechanism, which improves its capability to detect small and complex visual elements. The improved model is trained and thoroughly evaluated using experimental data. This study confirms that deep learning-based automatic extraction methods can effectively support visual communication design and provide valuable insights for its intelligent development.

Keywords: deep learning; visual communication; design elements; automatic extraction

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