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Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic

Rodrigo Carvalho Barbosa1, Muhammad Shoaib Ayub2, Renata Lopes Rosa1

  • 1Department of Computer Science, Federal University of Lavras, Minas Gerais 37200-000, Brazil.

Sensors (Basel, Switzerland)
|November 4, 2020
PubMed
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This study introduces a new deep learning model, PVIDNet, for accurate and fast detection of priority vehicles. This intelligent traffic system significantly reduces waiting times for emergency vehicles, improving urban traffic flow.

Area of Science:

  • Computer Vision and Artificial Intelligence
  • Intelligent Transportation Systems

Background:

  • Current traffic management systems often require human intervention, leading to inefficiencies.
  • Deep Learning (DL) shows promise for vehicle and traffic sign identification, but lacks specialized algorithms for priority vehicles.
  • Existing solutions often struggle with high accuracy, processing speed, and computational requirements.

Purpose of the Study:

  • To develop a novel, accurate, and lightweight vehicle detection model for intelligent traffic lights.
  • To specifically address the need for efficient classification of priority vehicles in urban environments.
  • To reduce the waiting time for priority vehicles through an integrated traffic control system.

Main Methods:

  • Development of the Priority Vehicle Image Detection Network (PVIDNet), a novel model based on YOLOV3.
Keywords:
deep learningimage detectionintelligent traffic lightvehicle classification

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  • Implementation of a lightweight design strategy for PVIDNet, utilizing a specific activation function to enhance processing speed.
  • Integration of PVIDNet with an intelligent traffic light system and a traffic control algorithm adhering to the Brazilian Traffic Code.
  • Creation of a dedicated database of Brazilian vehicle images for training and validation.
  • Evaluation of the system's effectiveness using the Simulation of Urban MObility (SUMO) tool.

    • Main Results:

    • PVIDNet achieved a detection accuracy exceeding 0.95.
    • The proposed system successfully reduced the waiting time for priority vehicles by up to 50%.
    • The lightweight design of PVIDNet contributed to decreased execution time.

    Conclusions:

    • The developed PVIDNet model offers a highly accurate and efficient solution for priority vehicle detection.
    • The integrated intelligent traffic light system significantly improves traffic flow by minimizing priority vehicle delays.
    • The study demonstrates a practical and effective approach to enhancing urban traffic management using deep learning.