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DCNet: Glass-Like Object Detection via Detail-Guided and Cross-Level Fusion

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14087))

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Abstract

Glass-like object detection aims to detect and segment whole glass objects from complex backgrounds. Due to the transparency of glass, existing detection methods often suffer from blurred object boundaries. Recently, several methods introduce edge information to boost performance. However, glass boundary pixels are extremely sparser than others. Using only edge pixels may negatively affect the glass detection performance due to the unbalanced distribution of edge and non-edge pixels. In this study, we propose a new detail-guided and cross-level fusion network (which we call DCNet) to tackle the issues of glass-like object detection. Firstly, we exploit label decoupling to get detail labels and propose a multi-scale detail interaction module (MDIM) to explore finer detail cues. Secondly, we design a body-induced cross-level fusion module (BCFM), which effectively guides the integration of features at different levels and leverages discontinuities and correlations to refine the glass boundary. Finally, we design an attention-induced aggregation module (AGM) that can effectively mine local pixel and global semantic cues from glass-like object regions, fusing features from all steps. Extensive experiments on the benchmark dataset illustrate the effectiveness of our framework.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China under Grant No. 62076058.

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Authors and Affiliations

  1. Northeastern University, Shenyang, 110819, China

    Jianhao Zhang, Gang Yang & Chang Liu

  2. DUT Artificial Intelligence Institute, Dalian, 116024, China

    Jianhao Zhang

Authors
  1. Jianhao Zhang
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  2. Gang Yang
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  3. Chang Liu
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Corresponding author

Correspondence to Gang Yang .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhang, J., Yang, G., Liu, C. (2023). DCNet: Glass-Like Object Detection via Detail-Guided and Cross-Level Fusion. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_38

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  • DOI: https://doi.org/10.1007/978-981-99-4742-3_38

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4741-6

  • Online ISBN: 978-981-99-4742-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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