In this advanced project, the Hoopad team, by combining drone technology and deep learning algorithms, has succeeded in developing an intelligent fire detection system that can be used in environmental monitoring operations, forests, and sensitive industrial areas. This system consists of a multirotor UAV with the ability to stabilize long-term flights and is equipped with high-resolution thermal and RGB cameras.
At the heart of this system is a convolutional neural network (CNN) optimized for detecting smoke, flame, and thermal anomalies in open environments. The AI model is trained on a proprietary dataset of over 21,000 fire and non-fire images and is capable of identifying critical points in live images with high accuracy. Image data is sent to the ground in real time and analysis results are displayed immediately through analytical software.
The following are some of the important benefits of this project:
- Fast and accurate fire detection in the early stages
- Instantly sending alerts to command centers and rapid alert systems
- Automatic monitoring of large areas without the need for a human operator
- Ability to archive and analyze data to examine dangerous patterns
- Customizable for forestry, oil, gas, and disaster management applications
This system is part of Hoopad’s vision for automating intelligent environmental monitoring with autonomous drones and can play a key role in reducing human and financial losses caused by fires. The design of this project is completely indigenous and is ready for development and implementation on various scales.
