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Related Experiment Videos

LDIE-FDNet: Lightweight dynamic image enhancement-enabled real-time fatigue driving detection network.

Chunyu Dong1, Tinglei Zhang1, Jing Liu1

  • 1Xi'an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation, School of Computer Science, Xijing University, Xi'an, China.

Plos One
|April 1, 2026
PubMed
Summary
This summary is machine-generated.

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A new lightweight network, LDIE-FDNet, improves fatigue driving detection accuracy, especially in low light. This real-time model enhances image quality and feature extraction, reducing errors and improving safety.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Automotive Safety

Background:

  • Existing fatigue driving detection models struggle with accuracy-real-time balance and low-light performance.
  • Inaccurate fatigue detection poses significant risks to road safety.

Purpose of the Study:

  • To design a Lightweight Dynamic Image Enhancement-Enabled Real-time Fatigue Driving Detection Network (LDIE-FDNet).
  • To improve accuracy and real-time performance in fatigue detection, particularly under low illumination conditions.
  • To enhance feature extraction and reduce computational costs.

Main Methods:

  • Image enhancement using Multi-Scale Retinex-Based Low-Light Image Enhancement Network (MSR-LIENET).
  • Lightweight feature extraction via GSConv_C3k2 module with variable convolution kernels.

Related Experiment Videos

  • Dynamic Hierarchical Feature Aggregation and Reconstruction Network (DHFAR-Net) with DySample and Semantic and Detail Infusion (SDI) for enhanced semantic and detail information.
  • Multi-level feature fusion and Powerfull-IoU (PIoU) loss function for improved detection and localization.
  • Integration of Maximum Closing Time (MCT) and Maximum Yawn Duration (MYD) metrics.

    • Main Results:

    • On the YawDD dataset: mAP increased by 0.6% to 99.2%, parameters reduced by 24%, GFLOPs increased by 14.3%, and FPS increased by 23.1%.
    • On the DMS dataset: mAP increased by 0.7% to 92.9%, parameters reduced by 24%, GFLOPs increased by 14.3%, and FPS increased by 20.5%.
    • The model demonstrates lower reasoning delay, memory occupation, and floating-point operations.

    Conclusions:

    • LDIE-FDNet effectively balances accuracy and real-time performance for fatigue driving detection.
    • The proposed method significantly improves detection accuracy, especially in challenging low-light conditions.
    • The network achieves superior efficiency with reduced parameters and computational load.