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Lightweight container code recognition based on multi-reuse feature fusion and multi-branch structure merger

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Abstract

In the field of global shipping, intelligent container code recognition has become a popular research topic. To address the issues of poor recognition accuracy and real-time performance in the complex port environment, this paper proposes a real-time container code recognition method based on YOLOV5. The proposed method consists of two stages: container code localization and container code recognition. First, for container code localization, the backbone component consists of MobileOne blocks to enhance feature extraction capability and accelerate inference speed. Second, for container code recognition, a lightweight MobileNetV3 is used as the backbone. Third, a feature extraction module, called Multiple Reuse Feature Pyramid Networks (MRFPN), is designed to extract sufficient semantic information. Experimental results show that the algorithm achieves accuracy rates of 96.5% and 93.5% in the container code localization and container code recognition stages, respectively, while significantly reducing the number of parameters, computations, and model size, and improving inference speed. Additionally, the proposed method performs better in terms of real-time performance and can meet the requirements of practical applications.

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Data availability

The data that support the fndings of this study are available from the corresponding author, [Guanghui Wang], upon reasonable request.

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Acknowledgements

This research was funded by the DIGWAY Industrial Co., Ltd. The authors greatly appreciate the support provided by this company. This research was funded by Major Science and Technology Project of Henan Province, Zhengzhou major scientific and technological innovation special project, and Key scientific research project plan of colleges and universities in Henan Province, under Grant Nos. 201110210300, 2019CXZX0050, and 21A510007.

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Authors and Affiliations

  1. School of Electronic Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China

    Dapeng Yang, Guanghui Wang, Mingtang Liu & Hao Zhang

  2. School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China

    Xiaokang Chen & Mengxiao Zhang

  3. School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China

    Shuang Yue

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  1. Dapeng Yang
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  5. Hao Zhang
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Contributions

DY: Conceptualization, Methodology, Software. GW: Data curation, Writing – original draft, Writing – review and editing. ML and SY: Visualization, Investigation. HZ and XC: Supervision. MZ: Software, Validation.

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Correspondence to Guanghui Wang.

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Yang, D., Wang, G., Liu, M. et al. Lightweight container code recognition based on multi-reuse feature fusion and multi-branch structure merger. J Real-Time Image Proc 20, 108 (2023). https://doi.org/10.1007/s11554-023-01364-x

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