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Real-Time Dynamic Intelligent Image Recognition and Tracking System for Rockfall Disasters.

Yu-Wei Lin1, Chu-Fu Chiu2, Li-Hsien Chen2

  • 1Department of Mechanical Engineering, National Taipei University of Technology, Taipei City 10608, Taiwan.

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|April 26, 2024
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Summary

A new deep learning system detects rockfalls in real-time, crucial for Taiwan

Keywords:
deep learninggeologymachine visionrock detectionrock tracking

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Area of Science:

  • Geosciences and Disaster Management
  • Computer Science and Artificial Intelligence

Background:

  • Taiwan's mountainous terrain and extreme weather lead to frequent rockfall disasters.
  • Existing disaster detection sensors record changes post-event, lacking real-time capabilities.
  • Rockfalls cause casualties, compensation claims, and disrupt transportation safety.

Purpose of the Study:

  • To develop and evaluate a real-time rockfall detection and tracking system.
  • To enhance disaster management and prevention strategies in rockfall-prone areas.

Main Methods:

  • Developed a system integrating YOLO (You Only Look Once) and image processing for real-time tracking.
  • Trained the system on a custom dataset of 2490 high-resolution RGB images.
  • Evaluated performance on 30 diverse rockfall scenario videos.

Main Results:

  • Achieved a mean Average Precision (mAP50) of 0.845 and mAP50-95 of 0.41.
  • Demonstrated a fast processing time of 125 ms.
  • System proved effective in quickly tracking and identifying hazardous rockfalls.

Conclusions:

  • The developed deep learning system offers a significant advancement for real-time rockfall detection.
  • Effective tracking and identification capabilities enhance disaster management and prevention efforts.
  • The system shows promise for improving transportation safety in hazardous regions.