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Multi-scale Edge-Based U-Shape Network for Salient Object Detection

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PRICAI 2021: Trends in Artificial Intelligence (PRICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13032))

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Abstract

Deep-learning based salient object detection methods achieve great improvements. However, there are still problems existing in the predictions, such as blurry boundary and inaccurate location, which is mainly caused by inadequate feature extraction and integration. In this paper, we propose a Multi-scale Edge-based U-shape Network (MEUN) to integrate various features at different scales to achieve better performance. To extract more useful information for boundary prediction, U-shape Edge Network modules are embedded in each decoder units. Besides, the additional down-sampling module alleviates the location inaccuracy. Experimental results on four benchmark datasets demonstrate the validity and reliability of the proposed method. Multi-scale Edge-based U-shape Network also shows its superiority when compared with 15 state-of-the-art salient object detection methods.

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China

    Han Sun, Yetong Bian & Ningzhong Liu

  2. School of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Huiyu Zhou

Authors
  1. Han Sun
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  2. Yetong Bian
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  3. Ningzhong Liu
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  4. Huiyu Zhou
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Corresponding author

Correspondence to Han Sun .

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Editors and Affiliations

  1. MIMOS Berhad, Kuala Lumpur, Malaysia

    Duc Nghia Pham

  2. Sirindhorn International Institute of Science and Technology, Thammasat University, Mueang Pathum Thani, Thailand

    Thanaruk Theeramunkong

  3. Data61, CSIRO, Brisbane, QLD, Australia

    Guido Governatori

  4. Department of Philosophy, Tsinghua University, Beijing, China

    Fenrong Liu

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Sun, H., Bian, Y., Liu, N., Zhou, H. (2021). Multi-scale Edge-Based U-Shape Network for Salient Object Detection. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13032. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_38

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  • DOI: https://doi.org/10.1007/978-3-030-89363-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89362-0

  • Online ISBN: 978-3-030-89363-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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