Title: Graphic design optimisation mechanism based on deep learning in smart cities

Authors: Yinan Chen

Addresses: Shenyang Normal University, Shenyang, Liaoning Province, China

Abstract: This article focuses on the background of smart cities, analyses and optimises urban graphic design based on deep learning, and proposes the improved UNet model based on the Coordinate Attention (CA-IUN). First, we improve the model based on UNet. The Improved UNet model (IUN) replaces some traditional convolutions in the encoding and decoding stages with dilated convolutions. Then, transposed convolution is used for upsampling, replacing traditional linear interpolation. We also design multi-scale fusion using phantom convolution and SENet. CA-IUN adds coordinate attention module to the encoder and decoder of IUN to focus on the specific positions of features. In addition, this article combines perceptual loss and smooth L1 loss function to train the network. Finally, experiments show that CA-IUN outperforms other models in optimising graphic design, indicating that CA-IUN can effectively achieve more refined and efficient graphic design optimisation in smart cities.

Keywords: deep learning; graphic design optimisation; smart cities.

DOI: 10.1504/IJCAT.2025.150323

International Journal of Computer Applications in Technology, 2025 Vol.77 No.3/4, pp.226 - 237

Received: 30 Sep 2024
Accepted: 18 Jun 2025
Published online: 09 Dec 2025 *

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