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Improved dense residual network with the coordinate and pixel attention mechanisms for helmet detection

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Abstract

Helmet detection in road surveillance images has become increasingly important with the increasing number of accidents involving two-wheeled electric vehicles and motorcycles. However, small detection targets and complex road environments make traditional helmet detection methods difficult. In this study, we propose an intelligent helmet detection model based on convolutional neural networks. To accurately capture the location of the helmet, we introduce the coordinate attention to obtain position information in the model. We thereafter introduce the pixel attention to enhance interpixel correlation and pixel-level feature filtering for the input images. These two attention mechanisms are combined to design the CPA module, and multi-CPA groups are constructed in a densely connected manner to obtain improved CPAG dense blocks. The proposed dual-attention mechanism effectively enhanced the weight of useful information and suppressed useless information. A dense block can improve the feature extraction ability and avoid information loss in the network. The CPAG dense block is inserted into the convolutional network model to obtain CPAG-Net as the detection network. To complete the system, we added a localization network to obtain the upper part of the rider. The localization network is accomplished using an improved YOLOv5s model in which we introduce an efficient channel attention mechanism to improve the localization ability for small targets. We compared the performance of the proposed method with those of several other methods. The results indicate that the proposed method is more robust than the other methods and has a higher accuracy for helmet detection in road surveillance images.

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Availability of data and materials

The data presented in this article are publicly available in EasyLink at https://easylink.cc/vfq7t3.

Code availability

The code presented in this article are publicly available in Github at https://github.com/MiJiang151/Helmet_Detection.git.

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Acknowledgements

This research was funded by the National Natural Science Foundation of China (Nos. 42374153, 42374149).

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 42374153 and 42374149).

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Author notes

  1. Jiang Mi, Jingrui Luo and Haixia Zhao have contributed equally to this work.

Authors and Affiliations

  1. School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China

    Jiang Mi & Jingrui Luo

  2. School of Mathematics, Xi’an Jiaotong University, Xi’an, China

    Haixia Zhao

  3. College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China

    Xingguo Huang

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, experimental preparation, and experimental analysis were performed by Jingrui Luo, Haixia Zhao, Xingguo Huang, and Jiang Mi. The first draft of the manuscript was written by Jiang Mi; the review and editing of the manuscript were completed by Jingrui Luo; and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xingguo Huang.

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Mi, J., Luo, J., Zhao, H. et al. Improved dense residual network with the coordinate and pixel attention mechanisms for helmet detection. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02205-4

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