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MFANet: Multi-scale feature fusion network with attention mechanism

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Abstract

In order to improve the detection accuracy of the network, it proposes multi-scale feature fusion and attention mechanism net (MFANet) based on deep learning, which integrates pyramid module and channel attention mechanism effectively. Pyramid module is designed for feature fusion in the channel and space dimensions. Channel attention mechanism obtains feature maps in different receptive fields, which divides each feature map into two groups and uses different convolutions to obtain weights. Experimental results show that our strategy boosts state-of-the-arts by 1–2% box AP on object detection benchmarks. Among them, the accuracy of MFANet reaches 34.2% in box AP on COCO dataset. Compared with the current typical algorithms, the proposed method achieves significant performance in detection accuracy.

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Funding

This work is supported in part by the National Key R &D Program of China under Grant 2017YFB1302400.

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Author notes

  1. G. Wang, X. Gan, Q. Cao and Q. Zhai: These authors contributed equally to this work.

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, 430068, China

    Gaihua Wang, Xin Gan, Qingcheng Cao & Qianyu Zhai

  2. Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, 430068, China

    Gaihua Wang

Authors
  1. Gaihua Wang
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  2. Xin Gan
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  3. Qingcheng Cao
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  4. Qianyu Zhai
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Contributions

Gaihua Wang, Xin Gan, Qing Caocheng and Qianyu Zhai conceived the experiments. Xin Gan and Qingcheng Cao conducted the experiments. All authors reviewed the manuscript.

Corresponding author

Correspondence to Xin Gan.

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Code and data are available.

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The experiments in this article are all realized through program operation, which will not cause harm to humans and animals and will not cause moral and ethical problems.

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Completed at Hubei University of Technology on December 14, 2021.

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Wang, G., Gan, X., Cao, Q. et al. MFANet: Multi-scale feature fusion network with attention mechanism. Vis Comput 39, 2969–2980 (2023). https://doi.org/10.1007/s00371-022-02503-4

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  • DOI: https://doi.org/10.1007/s00371-022-02503-4

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