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AgBFPN: Attention Guided Bidirectional Feature Pyramid Network for Object Detection

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Mobile Networks and Management (MONAMI 2022)

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Abstract

Object detection is increasingly in demand in IoT service applications. Deep learning based object detection algorithms are now in fashion. As the most popular multi-scale object detection network at present, Feature Pyramid Network achieves feature augmentation by fusing features of neighboring layers. It is widely used in the most advanced object detectors to detect objects of different scales. In this paper, we propose a new attention mechanism guided bidirectional feature pyramid architecture named AgBFPN to enhance the transfer of semantic and spatial information between each feature map. We design Channel Attention Guided Fusion(CAGF) Module and Spatial Attention Guided Fusion(SAGF) Module to enhance feature fusion. The CAGF mitigates the loss of information induced by channel reduction and better transfers the semantic information from high-level to low-level features. The SAGF passes the rich spatial information of shallow features into deep features. Our experiments show that AgBFPN achieves higher Average Precision for multi-scale object detection.

Supported by National Science Foundation of China (U19A2052, U1733111), National Key R &D Program of China (2021YFB1600500), Chengdu Science and Technology Project (2021-JB00-00025-GX), Key R &D Program of Sichuan Province (2020YFG0478), the Municipal Government of Quzhou under Grant Number 2021D012.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China

    Lanjie Jiang, Xiang Zhang, Ruijing Yang & Yudie Liu

  2. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, Zhejiang, China

    Lanjie Jiang, Xiang Zhang, Ruijing Yang & Yudie Liu

Authors
  1. Lanjie Jiang
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  2. Xiang Zhang
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  3. Ruijing Yang
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  4. Yudie Liu
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Corresponding author

Correspondence to Xiang Zhang .

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Editors and Affiliations

  1. Jiangxi Normal University, Nanchang, China

    Yuanlong Cao

  2. Toyohashi University of Technology, Toyohashi, Japan

    Xun Shao

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Jiang, L., Zhang, X., Yang, R., Liu, Y. (2023). AgBFPN: Attention Guided Bidirectional Feature Pyramid Network for Object Detection. In: Cao, Y., Shao, X. (eds) Mobile Networks and Management. MONAMI 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-031-32443-7_28

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  • DOI: https://doi.org/10.1007/978-3-031-32443-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32442-0

  • Online ISBN: 978-3-031-32443-7

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