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Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines

Author

Listed:
  • Yi Wang

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Zhengxiang He

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Liguan Wang

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

Due to complex background interference and weak space–time connection, traditional driver fatigue detection methods perform poorly for open-pit truck drivers. For these issues, this paper presents a driver fatigue detection method based on Libfacedetection and an LRCN. The method consists of three stages: (1) using a face detection module with a tracking method to quickly extract the ROI of the face; (2) extracting and coding the features; (3) combining the coding model to build a spatiotemporal classification network. The innovation of the method is to utilize the spatiotemporal features of the image sequence to build a spatiotemporal classification model suitable for this task. Meanwhile, a tracking method is added to the face detection stage to reduce time expenditure. As a result, the average speed with the tracking method for face detection on video is increased by 74% in comparison with the one without the tracking method. Our best model adopts a DHLSTM and feature-level frame aggregation, which achieves high accuracy of 99.30% on the self-built dataset.

Suggested Citation

  • Yi Wang & Zhengxiang He & Liguan Wang, 2021. "Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines," Mathematics, MDPI, vol. 9(22), pages 1-14, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2908-:d:679713
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    References listed on IDEAS

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    1. Han, Li & Jing, Huitian & Zhang, Rongchang & Gao, Zhiyu, 2019. "Wind power forecast based on improved Long Short Term Memory network," Energy, Elsevier, vol. 189(C).
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    1. Hasan Alkahtani & Zeyad A. T. Ahmed & Theyazn H. H. Aldhyani & Mukti E. Jadhav & Ahmed Abdullah Alqarni, 2023. "Deep Learning Algorithms for Behavioral Analysis in Diagnosing Neurodevelopmental Disorders," Mathematics, MDPI, vol. 11(19), pages 1-18, October.

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