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Vehicle Recognition and Driving Information Detection with UAV Video Based on Improved YOLOv5-DeepSORT Algorithm.

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  • 1Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology, Zhejiang Scientific Research Institute of Transport, No. 705 Dalongjuwu Rd., Hangzhou 311305, China.

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Summary

This study uses Unmanned aerial vehicles (UAVs) and a retrained YOLOv5 algorithm to accurately capture vehicle driving data. This approach enhances vehicle safety analysis by considering real-world driving habits.

Keywords:
UAV videoYOLOv5 algorithmrampvehicle recognitionvehicle track information

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Area of Science:

  • Engineering
  • Computer Science
  • Transportation Safety

Background:

  • Traditional vehicle driving simulations using ideal driver models are insufficient for assessing ramp skid resistance and safety.
  • Real-world driving habits significantly impact vehicle stability and safety, necessitating more accurate data collection methods.

Purpose of the Study:

  • To develop a method for collecting and extracting vehicle driving information from an Unmanned aerial vehicle (UAV) perspective.
  • To improve the accuracy and speed of vehicle detection and trajectory analysis for establishing realistic driver models.

Main Methods:

  • Utilized Unmanned aerial vehicles (UAVs) for capturing real-time vehicle driving video data.
  • Modified and retrained the "You Only Look Once" version 5 (YOLOv5) algorithm on a Google Collaboration platform using Python 3.7.12.
  • Integrated the trained YOLOv5 model into the DeepSORT algorithm, replacing Faster R-CNN, for enhanced vehicle detection and information extraction.
  • Employed coding to extract and smooth vehicle trajectory coordinates and used the frame difference method to calculate real-time speed.

Main Results:

  • The retrained YOLOv5 algorithm achieved satisfactory precision (P) and recall (R) rates, with an F1 score of 0.86.
  • The YOLOv5 model's loss function stabilized at a low level after 70 training epochs, indicating effective learning.
  • The enhanced DeepSORT algorithm with YOLOv5 improved detection accuracy and speed for extracting vehicle driving information from UAV footage.

Conclusions:

  • The proposed method effectively extracts crucial vehicle driving information, including trajectory and speed, from UAV perspectives.
  • This approach provides a foundation for building more accurate real driver models, crucial for assessing vehicle safety and ramp skid resistance.
  • The integration of UAVs and advanced computer vision algorithms offers a promising solution for intelligent transportation systems and safety analysis.