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Driving Behavior Recognition Algorithm Combining Attention Mechanism and Lightweight Network.

Lili Wang1, Wenjie Yao1, Chen Chen1

  • 1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.

Entropy (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved attention mechanism and lightweight network for driver behavior recognition, enhancing accuracy and reducing model size to prevent traffic accidents.

Keywords:
YOLOV4 modelattention mechanismdriving behavior recognitionfeature extraction

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Road Safety

Background:

  • Non-standard driving behaviors contribute to traffic accidents.
  • Traditional methods struggle with diverse behaviors, large action ranges, and low accuracy.
  • Accurate driver behavior recognition is crucial for accident prevention.

Purpose of the Study:

  • To develop an advanced driving behavior recognition algorithm.
  • To address limitations of existing detection methods, including accuracy and model efficiency.
  • To improve road safety by identifying and mitigating risky driving actions.

Main Methods:

  • An attention mechanism was integrated into the YOLOV4 model.
  • The feature extraction network and attention module structure were enhanced.
  • A dataset of 20,000 images was used to analyze and recognize 10 typical driving behaviors.

Main Results:

  • The proposed algorithm demonstrated good performance in accuracy.
  • The model achieved a reduced model size and fewer Floating Point Operations (FLOPs).
  • Ablation and comparison experiments validated the effectiveness of the improved attention mechanism and lightweight network.

Conclusions:

  • The fusion of an improved attention mechanism and lightweight network offers superior performance for driving behavior recognition.
  • This approach effectively balances accuracy, model size, and computational efficiency.
  • The developed algorithm shows significant potential for real-world application in enhancing road safety.