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Scalable Fire and Smoke Segmentation from Aerial Images Using Convolutional Neural Networks and Quad-Tree Search.

Gonçalo Perrolas¹, Milad Niknejad¹, Ricardo Ribeiro¹

¹Instituto de Sistemas e Robótica, Instituto Superior Tecnico, University of Lisbon, 1049-001 Lisbon, Portugal.

Sensors (Basel, Switzerland)

|March 10, 2022

Summary

This summary is machine-generated.

This study introduces a new method for early fire and smoke detection in high-resolution images. It uses a multi-resolution quad-tree search with Convolutional Neural Networks (CNNs) for more accurate and efficient fire segmentation.

Keywords:

aerial images convolutional neural networks fire detection smoke detection wildfire

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Area of Science:

Computer Vision
Artificial Intelligence
Fire Safety Engineering

Background:

Autonomous systems and Convolutional Neural Networks (CNNs) show promise in fire and smoke detection.
Downsizing high-resolution images for CNNs can cause early-stage fire regions to be missed.
Efficient processing of high-resolution imagery is crucial for timely fire detection.

Purpose of the Study:

To develop a novel method for segmenting fire and smoke regions in high-resolution images.
To improve the accuracy and computational efficiency of fire and smoke detection systems.
To address the challenge of detecting small fire ignitions in early stages.

Main Methods:

A multi-resolution iterative quad-tree search algorithm is proposed.
The algorithm intelligently applies classification and segmentation CNNs to informative image regions.
This approach optimizes processing for high-resolution aerial imagery.

Main Results:

The proposed method achieves higher accuracy in detecting and segmenting fire and smoke.
It is computationally more efficient than processing entire high-resolution images.
The system effectively segments small, early-stage fire incidents.

Conclusions:

The novel quad-tree based CNN approach enhances fire and smoke detection accuracy and efficiency.
This method is particularly effective for identifying small fire regions in high-resolution aerial surveillance.
The system offers tunable parameters for precision, making it adaptable for various firefighting scenarios.