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Real-Time Object Detection from UAV Inspection Videos by Combining YOLOv5s and DeepStream.

Shidun Xie1, Guanghong Deng1, Baihao Lin1

  • 1Guangdong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, China.

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|June 27, 2024
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Summary
This summary is machine-generated.

This study introduces an AI-powered system for high-altitude unmanned aerial vehicle (UAV) inspections, improving object detection accuracy and speed. The lightweight YOLOv5s model, integrated with an enhanced DeepStream framework, enables efficient real-time automated inspections.

Keywords:
DeepStreamUAVsYOLOv5object detectionroute planning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • High-altitude inspections using unmanned aerial vehicles (UAVs) face challenges including weather interference, signal disruption, and reduced object visibility.
  • Existing methods struggle with real-time data processing and reliable object identification in complex aerial environments.

Purpose of the Study:

  • To develop an automated UAV inspection system leveraging artificial intelligence for enhanced object detection.
  • To create a lightweight and efficient AI model suitable for edge deployment on UAVs.
  • To improve the robustness and usability of the inspection system through framework modifications.

Main Methods:

  • A combination of a UAV system scheduling platform and artificial intelligence object detection was employed.
  • The YOLOv5s model was trained on a diverse vehicle dataset, achieving high accuracy metrics (mAP50: 93.2%, mAP50-95: 71.7%).
  • The DeepStream framework was modified with HTTP communication, asynchronous alarm functions, and improved video streaming recovery for real-time deployment.

Main Results:

  • The YOLOv5s model demonstrated a small file size (13.76 MB) and rapid detection speed (11.26 ms per image), ideal for edge computing.
  • The integrated system successfully performed automatic UAV inspections with improved efficiency and reliability.
  • Framework enhancements facilitated simultaneous user access and resilient video stream management.

Conclusions:

  • The developed AI-driven UAV inspection system offers a lightweight, efficient, and robust solution for real-time aerial monitoring.
  • The optimized YOLOv5s model and enhanced DeepStream framework provide a scalable platform for automated inspections in challenging conditions.
  • This approach significantly advances the capabilities of UAVs in high-altitude surveillance and data collection.