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A Multi-focus Image Fusion Network Deployed in Smart City Target Detection
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  • Haojie Zhao,
  • Shuang Guo,
  • Gwanggil Jeon,
  • Xiaomin Yang
Haojie Zhao
Sichuan University College of Electronics and Information Engineering
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Shuang Guo
Sichuan University College of Electronics and Information Engineering
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Gwanggil Jeon
Incheon National University
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Xiaomin Yang
Sichuan University College of Electronics and Information Engineering

Corresponding Author:arielyang@scu.edu.cn

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Abstract

In the global monitoring of smart cities, the demands of global object detection systems based on cloud and fog computing in intelligent systems can be satisfied by photographs with globally recognized properties. Nevertheless, conventional techniques are constrained by the imaging depth of field and can produce artifacts or indistinct borders, which can be disastrous for accurately detecting the object. In light of this, this paper proposes an artificial intelligence-based gradient learning network that gathers and enhances domain information at different sizes in order to produce globally focused fusion results. Gradient features, which provide a lot of boundary information, can eliminate the problem of border artifacts and blur in multi-focus fusion. The multiple-receptive module (MRM) facilitates effective information sharing and enables the capture of object properties at different scales. In addition, with the assistance of the global enhancement module (GEM), the network can effectively combine the scale features and gradient data from various receptive fields and reinforce the features to provide precise decision maps. Numerous experiments have demonstrated that our approach outperforms the seven most sophisticated algorithms currently in use.
Submitted to Expert Systems
16 Apr 2024Reviewer(s) Assigned
08 May 2024Review(s) Completed, Editorial Evaluation Pending
11 May 2024Editorial Decision: Revise Major
30 May 2024Reviewer(s) Assigned