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TRCA-Net: stronger U structured network for human image segmentation

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Abstract

Human image segmentation has been a practical and active research topic due to its wide range of potential application. There are some previous studies on manual, semi-automatic and automatic segmentation methods to investigate the semantic segmentation of human parts fully for real-world human analysis scenarios, but further research is still needed. This paper presents a novel semantic segmentation network, named TRCA-Net, for human image segmentation tasks. Having the TransUNet as the backbone, TRCA-Net incorporates Res2Net and Coordinate Attention to improve the performance. Res2Net blocks and Transformer can obtain better feature maps by encoding the input images. The Coordinate Attention in the decoder aggregates and upsamples the encoded feature maps, and connects to the high-resolution CNN feature maps for gaining accurate segmentation. The TRCA-Net can enhance finer details by recovering local spatial information. We compare the TRCA-Net with state-of-the-art (SOAT) semantic segmentation networks: the original U-Net, DeepLabv3+, and TransUNet. The experiment results have demonstrated that our proposed TRCA-Net outperforms these networks.

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Data availability statements

All data generated or analyzed during this study are included in this published article [3] and its supplementary information files.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 52171292, 51939001), Dalian Outstanding Young Talents Project (Grant No. 2022RJ05).

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Authors and Affiliations

  1. College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China

    Li-Ying Hao, Zhengkai Yang & Yun-Peng Liu

  2. Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada

    Chao Shen

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  1. Li-Ying Hao
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  2. Zhengkai Yang
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  3. Yun-Peng Liu
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  4. Chao Shen
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Correspondence to Chao Shen.

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Hao, LY., Yang, Z., Liu, YP. et al. TRCA-Net: stronger U structured network for human image segmentation. Neural Comput & Applic 35, 9627–9635 (2023). https://doi.org/10.1007/s00521-023-08199-4

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