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Traffic flow detection method based on improved SSD algorithm for intelligent transportation system.

Guodong Su1, Hao Shu2

  • 1School of Physics and Optoelectronics, Xiangtan University, Xiangtan, China.

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Summary

This study introduces an intelligent vehicle flow detection model using an improved single shot multi box detector algorithm for enhanced intelligent transportation systems. The model achieves superior accuracy and precision in vehicle flow detection compared to traditional methods.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Transportation Engineering

Background:

  • Intelligent transportation systems require advanced vehicle flow detection.
  • Traditional methods struggle with high accuracy and speed requirements.
  • Next-generation communication systems increase demands on traffic monitoring.

Purpose of the Study:

  • To develop an intelligent vehicle flow detection model with improved accuracy and speed.
  • To address limitations of traditional traffic flow detection methods.
  • To enhance vehicle flow monitoring in intelligent transportation systems.

Main Methods:

  • Integration of an improved Inception module into the single shot multi box detector algorithm.
  • Construction of an intelligent vehicle flow detection model.
  • Experimental validation using traffic flow statistics and various image datasets.

Main Results:

  • The improved algorithm exhibited the fastest convergence speed.
  • Achieved 93.6% accuracy and 96.0% precision on the entire test set, outperforming comparison algorithms.
  • Demonstrated highest statistical accuracy in traffic flow statistics, with average accuracy and precision of 96.9% and 96.8% respectively.

Conclusions:

  • The developed intelligent vehicle flow detection model offers higher detection accuracy.
  • The model's calculation speed is comparable to traditional methods and significantly faster than manual monitoring.
  • The model is beneficial for traffic flow management in intelligent transportation systems.