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This study compares YOLO algorithms for Unmanned Aerial Vehicle (UAV) Highway Distress Detection (HDD). YOLOv11-n excels in efficiency and model size, making it ideal for real-time applications.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Transportation Engineering

Background:

  • Unmanned Aerial Vehicle (UAV) based Highway Distress Detection (HDD) presents challenges in accuracy and efficiency.
  • The YOLO (You Only Look Once) algorithm series offers potential solutions for automated HDD.

Purpose of the Study:

  • To conduct a comparative analysis of various YOLO algorithms for UAV-based HDD.
  • To provide a reference for selecting optimal YOLO models for highway infrastructure monitoring.

Main Methods:

  • Comparative evaluation of YOLOv5, YOLOv7, YOLOv8, YOLOv9, and YOLOv10 algorithms.
  • Assessment of detection accuracy (precision, recall) and detection efficiency (speed, model size, computational complexity).

Main Results:

  • YOLOv5-l and YOLOv9-c demonstrated the highest detection accuracy.
  • YOLOv10-n, YOLOv7-t, and YOLOv11-n led in detection efficiency.
  • YOLOv11-n offered the best balance of efficiency, small model size, and low computational complexity for real-time HDD.
  • YOLOv5-n and YOLOv8-n showed the highest relative compression degrees.

Conclusions:

  • YOLOv11-n is a highly promising model for embedded real-time UAV-based Highway Distress Detection.
  • Model architecture, particularly backbone changes in YOLOv9 and depth/width multiples in YOLOv5, significantly impacts accuracy.
  • Specific YOLO versions offer distinct advantages for accuracy, efficiency, or model size in HDD applications.