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Summary

This study introduces a low-cost, real-time vehicle recognition system using edge computing for accurate urban noise mapping. The system effectively classifies vehicles, significantly improving traffic monitoring accuracy and cost-effectiveness for environmental acoustic modeling.

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CNOSSOS-EU classificationedge computingnoise assessment supportquantized convolutional neural networksreal-time vehicle detectiontraffic flow monitoring

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Area of Science:

  • Environmental acoustics
  • Urban planning
  • Computer vision

Background:

  • Accurate urban noise mapping relies on detailed traffic data compatible with acoustic models like CNOSSOS-EU.
  • Current traffic monitoring systems often fall short in categorization, cost-efficiency, and scalability for widespread use.

Purpose of the Study:

  • To develop a cost-effective, real-time multi-vehicle recognition system for urban noise mapping.
  • To enable precise vehicle classification for aggregation according to CNOSSOS-EU standards using edge computing.

Main Methods:

  • Integration of a quantized YOLOv8 convolutional neural network (CNN) with a tracking algorithm on Raspberry Pi 4 and Coral TPU.
  • Training the CNN on a 15,000-image dataset with 8-bit post-training quantization for optimized inference speed.
  • Real-time detection and classification of vehicles into five distinct classes.

Main Results:

  • Achieved an inference speed of 14 FPS and a mean Average Precision (mAP@50) of 92.2% in daytime conditions.
  • Demonstrated robust performance on embedded devices, suitable for resource-constrained environments.
  • In a case study, the system achieved a 6.6% weighted percentage error, vastly outperforming a commercial solution (59.9%) and approaching manual accuracy (1.4%).

Conclusions:

  • The developed edge computing system offers a highly accurate and cost-effective solution for real-time urban traffic monitoring.
  • This technology bridges the gap between manual data collection and current automated systems, enhancing urban noise mapping capabilities.
  • The system's efficiency and accuracy make it suitable for large-scale deployment in smart city initiatives.