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EffRes-DrowsyNet: A Novel Hybrid Deep Learning Model Combining EfficientNetB0 and ResNet50 for Driver Drowsiness

Sama Hussein Al-Gburi1, Kanar Alaa Al-Sammak1, Ion Marghescu1

  • 1Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania.

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A new hybrid deep learning model, EffRes-DrowsyNet, effectively detects driver drowsiness using video analytics. This advanced system enhances road safety by identifying fatigue signs early, improving reaction times and cognitive performance.

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EfficientNetB0ResNet50automated fatigue recognitiondriver drowsiness detectiondriver monitoring systemshybrid deep learningimage classificationmachine learning in transportationreal-time video analyticssafety-critical systems

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Driver drowsiness is a significant cause of road accidents, leading to delayed reactions and impaired cognitive function.
  • Current driver monitoring systems require improvement for real-time, accurate fatigue detection.

Purpose of the Study:

  • To introduce EffRes-DrowsyNet, a novel hybrid deep learning model for early driver drowsiness detection.
  • To evaluate the model's performance across diverse datasets and conditions.

Main Methods:

  • Developed EffRes-DrowsyNet by integrating EfficientNetB0 and ResNet50 architectures.
  • Utilized video-based analytics for fatigue detection.
  • Conducted experiments on SUST-DDD, YawDD, and NTHU-DDD benchmark datasets.

Main Results:

  • Achieved high accuracy (97.71%) on SUST-DDD, (92.73%) on YawDD, and (95.14%) on NTHU-DDD.
  • Demonstrated superior generalization and classification performance in various settings.
  • Obtained high precision (98.07%) and recall (97.33%) on SUST-DDD.

Conclusions:

  • Hybrid deep learning models are effective for real-time, safety-critical applications like driver monitoring.
  • EffRes-DrowsyNet offers a scalable and adaptable solution for attention-critical domains.
  • The model shows promise for enhancing automotive safety systems and beyond.