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Related Experiment Video

Updated: Jun 21, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Domain-robust vision transformer with hierarchical swin encoding for explainable low-latency driver drowsiness

Al Rafy1, Md Najmul Gony2, Md Mashfiquer Rahman3

  • 1College of Technology and Engineering, Westcliff University, Irvine, CA, 92614, USA.

Scientific Reports
|June 19, 2026
PubMed
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A new lightweight Vision Transformer (ViT) model, Light-VTD, accurately detects driver drowsiness from facial images. It offers explainable heatmaps and low-latency performance on devices like Raspberry Pi 4.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Driver drowsiness is a major cause of road accidents globally.
  • Existing fatigue detection systems struggle with generalization, transparency, and low-power deployment.
  • There is a need for accurate, interpretable, and efficient fatigue monitoring solutions.

Purpose of the Study:

  • To introduce Light-VTD, a novel lightweight Vision Transformer (ViT) architecture for accurate and explainable driver fatigue detection.
  • To improve upon existing methods by enhancing local feature extraction, global context capture, and spatial consistency.
  • To enable real-time, low-power fatigue monitoring with visual explanations.

Main Methods:

  • Developed Light-VTD, incorporating fused-MBConv blocks, a position-aware token mixer, and multi-path token fusion.
Keywords:
Drowsiness detectionEmbedded systemsExplainable AI (XAI)Token fusionVision transformer (ViT)

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  • Adapted Gradient-weighted Class Activation Mapping (Grad-CAM) for interpretable attention heatmaps.
  • Trained and evaluated the model on a large-scale dataset (167,000+ images) from four diverse public datasets.

    • Main Results:

    • Achieved 99.2% intra-dataset accuracy and over 93% cross-dataset transfer accuracy.
    • Outperformed existing ViT baselines by 2-4% in accuracy.
    • Demonstrated low-latency inference (<120 ms) suitable for low-power devices (Raspberry Pi 4).

    Conclusions:

    • Light-VTD provides a robust, interpretable, and deployable solution for driver fatigue detection.
    • The model's explainability through Grad-CAM aids in understanding fatigue indicators.
    • Potential applications include Advanced Driver Assistance Systems (ADAS), workplace safety, and portable diagnostics.