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An Enhanced Detector for Vulnerable Road Users Using Infrastructure-Sensors-Enabled Device.

Jian Shi1, Dongxian Sun1, Minh Kieu2

  • 1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

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|January 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved detector for vulnerable road users (VRUs) using infrastructure sensors. The enhanced model achieves better detection accuracy and faster real-time performance for intelligent traffic systems.

Keywords:
VRU detectioninfrastructure-sensors-enabled engineeringmodel lightweightobject detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Intelligent Transportation Systems

Background:

  • Precise and real-time detection of vulnerable road users (VRUs) is essential for intelligent traffic monitoring.
  • Existing VRU detection methods face inefficiencies, particularly with small targets in high-resolution images.

Purpose of the Study:

  • To develop an enhanced detector for accurate and efficient VRU detection using infrastructure-sensor-enabled devices.
  • To improve feature extraction for small targets and reduce model complexity for real-time applications.

Main Methods:

  • Utilized a lightweight backbone network with a parameterless attention mechanism for enhanced feature extraction.
  • Implemented a streamlined 'neck' and dynamic detection head with a pruning algorithm for model compression.
  • Deployed the model on the Hisilicon_Hi3516DV300 platform using the De_VRU dataset.
  • Conducted ablation studies using YOLOv7-tiny as a baseline on BDD100K and LLVIP datasets.

Main Results:

  • Achieved over 12% improvement in mAP@50 metric.
  • Reduced model parameter count by more than 40%.
  • Decreased inference time by 50%, enabling real-time detection.

Conclusions:

  • The enhanced detector demonstrates significant improvements in accuracy and efficiency for VRU detection.
  • The model's compact architecture and fast inference time make it suitable for practical deployment in intelligent traffic systems.
  • The study highlights the potential of advanced deep learning techniques for enhancing road safety.