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Multipurpose deep learning-powered UAV for forest fire prevention and emergency response.

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This study introduces a custom Unmanned Aerial Vehicle (UAV) for forest rescue, featuring real-time video, GPS, and deep learning for fire prediction. This advanced drone enhances emergency response efficiency and safety in remote areas.

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Environmental Monitoring

Background:

  • Forest environments pose significant challenges for traditional search and rescue operations.
  • Early detection and rapid response are critical for managing forest fires and locating individuals in distress.
  • Existing technologies often lack the integrated capabilities for real-time data collection, communication, and intervention.

Purpose of the Study:

  • To design and develop a customized Unmanned Aerial Vehicle (UAV) for enhanced rescue and safety operations in forest sectors.
  • To integrate advanced sensors and artificial intelligence for real-time environmental monitoring and emergency prediction.
  • To create a user-friendly system for efficient and effective emergency response in challenging terrains.

Main Methods:

  • Construction of a durable F450 quadcopter frame with high-performance brushless motors and a KK2.1 Flight Control Board for stability.
  • Integration of a Raspberry Pi camera for real-time video streaming and a Neo-6M GPS module for accurate localization.
  • Implementation of a GSM module for communication, a motor-controlled first aid kit, and a DHT 11 sensor for environmental data collection.
  • Application of deep learning models, including Artificial Neural Networks (ANN) and Generative Adversarial Networks (GANs), for forest fire prediction using collected data.
  • Interfacing all components with a Raspberry Pi4 and a Graphical User Interface (GUI) for streamlined control and data management.

Main Results:

  • The UAV achieved a 90-minute runtime, demonstrating sustained operational capability.
  • Real-time video streaming and GPS localization (2.5m accuracy) facilitated efficient identification and location of individuals.
  • The DHT 11 sensor provided accurate temperature (+/- 2°C) and humidity (+/- 5%) data.
  • Deep learning models, particularly GANs, achieved a 90.7% accuracy in real-time forest fire prediction.
  • The integrated system provided a smooth user experience for quick and effective emergency response.

Conclusions:

  • The customized UAV system offers a robust and integrated solution for improving safety and rescue operations in forest environments.
  • The combination of advanced hardware and AI-driven prediction significantly enhances the speed and effectiveness of emergency responses.
  • This technology has the potential to revolutionize forest management and emergency services by providing real-time situational awareness and rapid intervention capabilities.