Intelligent and Converged Networks 2022, 3(3): 294-308 https://doi.org/10.23919/ICN.2022.0022

Open Access | Issue | Published: 30 September 2022

A DAC-CLGD-Danet network based method for defaced image segmentation

Show Author's Information Hide Author's Information Pengbo Li^¹, Gang Li^¹, Yibin He^¹, Ling Zhang^¹(

), Yuanjin Sun^¹, Fayun Guo^²

1College of Software, Taiyuan University of Technology, Taiyuan 030024, China

2Information Technology Department, Shanxi Taisen Technology Co. Ltd., Taiyuan 030082, China

Keywords:

deep learning, neural networks, image segmentation, Danet, defaced images

Cite this article:

Li P, Li G, He Y, et al. A DAC-CLGD-Danet network based method for defaced image segmentation. Intelligent and Converged Networks, 2022, 3(3): 294-308. https://doi.org/10.23919/ICN.2022.0022

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Abstract

Based on the problems of high noise, lower contrast, and complex features in defaced images and the low accuracy of existing defaced image segmentation techniques, this paper proposes a defaced image segmentation algorithm based on DAC-CLGD-Danet. Firstly, a CBDNet asymmetric blind denoising network is used for noise-containing defaced images, and natural and synthetic images are trained together to model the image noise and enhance the denoising ability of natural noise. Secondly, Danet is used as the base network. A Dense Atrous Convolution module (DAC) is added to the dual attention mechanism module to extend the perceptual domain of deep convolution, reduce image feature loss, and enhance the representation of global information and edge features of defaced images; Cross-Level Gating Decoder module (CLGD) is introduced to lighten the segmentation network, enhance image context aggregation, and produce accurate semantic segmentation. The experimental results demonstrated that the method in this paper has a significant effect on the HRF dataset and Cityscapes dataset, with a significant improvement compared with FCN, UNet, and SETR models, with Intersection over Union (IoU) improved by 9.81% and Mean Intersection over Union (mIoU) improved by 3.01% compared with UNet.

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A DAC-CLGD-Danet network based method for defaced image segmentation

Show Author's information Hide Author's Information Pengbo Li^¹, Gang Li^¹, Yibin He^¹, Ling Zhang^¹(

), Yuanjin Sun^¹, Fayun Guo^²

1College of Software, Taiyuan University of Technology, Taiyuan 030024, China

2Information Technology Department, Shanxi Taisen Technology Co. Ltd., Taiyuan 030082, China

Abstract

Keywords: deep learning, neural networks, image segmentation, Danet, defaced images

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Publication history

Received: 27 October 2022

Revised: 24 November 2022

Accepted: 02 December 2022

Published: 30 September 2022

Issue date: September 2022

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Acknowledgements

Acknowledgment

This work was supported by the Central Leading Local Special Foundation of Shanxi Province (Nos. YDZJSX2021C004 and YDZJSX2022A016), and the Natural Science Foundation of Shanxi Province (No. 20210302124554);

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This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/