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Dense Attention Fusion Network for Object Counting in IoT System

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Abstract

IoT has been overwhelmingly empowered by the rapid development of big-data ecosystems, such as remote sensing technology which runs all the time in obtaining accurate and high-quality images to facilitate the subsequent image processing and content analysis in embedded devices. Object counting, which aims to estimate the number of objects in a captured image, is one of the most crucial tasks among multimedia data and wireless network. However, there are enormous inherent factors that seriously degrade the counting performance in remote sensing, e.g. the background clutter, scale variation, and orientation arbitrariness. In this paper, we tackle the aforementioned problems in a divide-and-conquer manner by devising the dense attention fusion network (DAFNet). Specifically, we introduce an iterative attention fusion (IAF) module, which mainly relies on the multiscale channel attention (MCA) unit, to alleviate the side effect caused by background clutter. Meanwhile, to overcome the intrinsic scale variations, we build a dense spatial pyramid (DSP) module to consider the hierarchical information obtained under diverse receptive fields. Finally, we stack deformable convolution layers to deal with the orientation arbitrariness. The synergy of the proposed IAF and DSP modules substantially promotes the effectiveness of the proposed DAFNet, which can be demonstrated by the notable superiority in extensive experiments on the remote sensing counting datasets against state-of-the-art competitors.

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Funding

This work is supported in part by the National Natural Science Foundation of China (Nos. 61601266 and 61801272) and National Natural Science Foundation of Shandong Province (Nos. ZR2021QD041 and ZR2020MF127).

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, 255000, China

    Xiangyu Guo, Mingliang Gao & Wenzhe Zhai

  2. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK

    Qilei Li

  3. Korea Conformity Laboratories, Gasan digital 1-ro, Geumcheon-gu, Seoul, Republic of Korea

    Kyu Hyung Kim

  4. Department of Embedded Systems Engineering, Incheon National University, Incheon, 22012, Republic of Korea

    Gwanggil Jeon

Authors
  1. Xiangyu Guo
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  2. Mingliang Gao
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  5. Kyu Hyung Kim
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Contributions

Xiangyu Guo: Conceptualization, Methodology, Data Curation, and Writing - Original Draft. Mingliang Gao: Supervision, Formal analysis, Investigation, and Funding Acquisition. Wenzhe Zhai: Data Curation, Data Visualization, and Investigation. Qilei Li: Investigation, and Software. Kyu Hyung Kim: Formal Analysis, and Writing -Review & Editing. Gwanggil Jeon: Validation, and Writing -Review & Editing.

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Correspondence to Mingliang Gao.

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Guo, X., Gao, M., Zhai, W. et al. Dense Attention Fusion Network for Object Counting in IoT System. Mobile Netw Appl 28, 359–368 (2023). https://doi.org/10.1007/s11036-023-02090-1

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