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Distracted driving detection based on the improved CenterNet with attention mechanism

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Abstract

Distracted driving detection has many significant application scenarios in intelligent transportation, driver assistance, and other fields. However, these distracted behaviors are difficult to be recognized due to the variable background and different scale targets. To solve these problems, a distracted driving detection scheme is proposed based on the improved CenterNet with attention mechanism in this paper. Given the complexity of driving environments, an image classification method was first designed to divide the images into person and unmanned areas, which can reduce the interference in unmanned situations. And then a novel attention mechanism module was introduced into CenterNet to improve its detection ability for small targets. Numerous experiments were conducted with a public dataset and newly built targeted dataset that included three categories of distracted driving behaviors with 6481 pictures. The results demonstrated that, the proposed scheme can detect distracted behaviors in real time while driving with a mean average precision (mAP) of 97.0%, which outperforms some representative detection methods, such as CornerNet, YOLO v3 and YOLO v4.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61671412), Zhejiang Provincial Natural Science Foundation of China (Grant No. LY19F010002, LY21F010014), Natural Science Foundation of Ningbo, China (Grant No. 2018A610053, 202003N4323), Ningbo Municipal Projects for Leading and Top Talents (Grant No. NBLJ201801006), General Scientific Research Project of Zhejiang Education Department (Grant No. Y201941122), School level scientific research and innovation team project, and Fundamental Research Funds for Zhejiang Provincial Colleges and Universities.

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Authors and Affiliations

  1. Ningbo Key Lab of Digital Signal Processing, Zhejiang Wanli University, Ningbo, 315100, China

    Qingqing Zhang, Zhongjie Zhu, Yongqiang Bai, Guanglong Liao & Tingna Liu

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  1. Qingqing Zhang
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  2. Zhongjie Zhu
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  3. Yongqiang Bai
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  4. Guanglong Liao
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  5. Tingna Liu
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Contributions

Qingqing Zhang: Ideas; Creation of models; Software; Zhongjie Zhu: Conceptualization; Investigation; Review & Editing; Funding acquisition; Yongqiang Bai: Methodology; Formal analysis; Funding acquisition; Validation; Review & Editing; Guanglong Liao: Evidence collection; Original Draft; Evidence collection; Tingna Liu: Visualization; Data Curation.

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Correspondence to Zhongjie Zhu or Yongqiang Bai.

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Zhang, Q., Zhu, Z., Bai, Y. et al. Distracted driving detection based on the improved CenterNet with attention mechanism. Multimed Tools Appl 81, 7993–8005 (2022). https://doi.org/10.1007/s11042-022-12128-3

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  • DOI: https://doi.org/10.1007/s11042-022-12128-3

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