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Dual-Branch Enhanced Network for Change Detection

  • Research Article-Electrical Engineering
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Abstract

Change detection is an essential task in intelligent monitoring, and the accuracy of detection is of central importance for subsequent target tracking and recognition. However, a series of challenges such as illumination change, severe weather, shadow, and camera jitter have brought great troubles. To reduce the impact of these factors, we propose a novel model, called dual-branch enhanced network (DBEN), which can simultaneously extract enough spatial features and context information. Specifically, we design a recurrent gated bottleneck module to get high-level features, and build the global attention module as an auxiliary branch to obtain fine resolution details. Moreover, we also propose a gated residual dense module to enhance feature expression by reconstructing the combined information. Meanwhile, a weighted loss function is designed to optimize the network. The proposed DBEN is verified on CDnet2014, DAVIS and AICD, which are three large-scale change detection datasets. Experimental results show that the proposed model is competitive in overall performance.

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Acknowledgements

This work was supported in part by the Self-Determined Research Funds of Central China Normal University (CCNU) from the Colleges’ Basic Research and Operation of the Ministry of Education (MOE) under Grant CCNU18TS042.

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Authors and Affiliations

  1. College of Physical Science and Technology, Central China Normal University, Wuhan, China

    Hongrui Zhang & Shaocheng Qu

  2. Department of Special Technology, Special Operations College of the PLA, Guilin, China

    Huan Li

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  1. Hongrui Zhang
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  2. Shaocheng Qu
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Correspondence to Shaocheng Qu.

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Zhang, H., Qu, S. & Li, H. Dual-Branch Enhanced Network for Change Detection. Arab J Sci Eng 47, 3459–3471 (2022). https://doi.org/10.1007/s13369-021-06306-y

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