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A Comparative Study of Four YOLO-Based Models for Distracted Driving Detection

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Advances in Internet, Data & Web Technologies (EIDWT 2024)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 193))

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Abstract

In general, the accidents are often attributed to the decline of driving skills and reduced attention among the aging population. Moreover, the rise of infotainment features in vehicles has led to loss of concentration while driving. To address this issue, we previously developed an intelligent driver assistance system to identify instances of distracted driving. This paper focuses on enhancing the detection accuracy of distracted driving by utilizing an upgraded version of our system equipped with different detectors. The system is designed to recognize various distracted driving behaviors, particularly focusing on the driver hand movements. From the evaluation, we observed that while models like YOLOv8 show improvements in detecting specific actions like steering wheel and IVI operations, challenges remain in accurately identifying other crucial activities such as cell phone usage and general hand movements.

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Authors and Affiliations

  1. Graduate School of Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811-0295, Japan

    Naoki Tanaka & Hibiki Tanaka

  2. Department of Information and Communication Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811-0295, Japan

    Makoto Ikeda & Leonard Barolli

Authors
  1. Naoki Tanaka
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  2. Hibiki Tanaka
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  3. Makoto Ikeda
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  4. Leonard Barolli
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Corresponding author

Correspondence to Makoto Ikeda .

Editor information

Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Tanaka, N., Tanaka, H., Ikeda, M., Barolli, L. (2024). A Comparative Study of Four YOLO-Based Models for Distracted Driving Detection. In: Barolli, L. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 193. Springer, Cham. https://doi.org/10.1007/978-3-031-53555-0_34

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