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Deep Learning approach applied to drone imagery for the automatic detection of forest fire
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Orientador(es)
Resumo(s)
Wildfires are one of the world's most costly and deadly natural disasters, damaging millions
of hectares of vegetation and threatening the lives of people and animals. The risks to civilian
agents and task forces are particularly high, which emphasizes the value of leveraging
technology to minimize their impacts on nature and people. The use of drone imagery coupled
with deep learning for automated fire detection can provide new solutions to this problem,
limiting the damage that result.
In this context, our work aims to implement a solution for the automatic detection of forest
fires in real time by exploiting convolutional neural networks (CNN) on drone images based
on classification and segmentation models.
The methodological approach followed in this study can be broken down into three main
steps: First, the comparison of two models, namely Xception Network and EfficientNetB2, for
the classification of images captured during a forest burn into 'Fire' or 'No_Fire' classes. Then
we will proceed to the segmentation of the images belonging to the 'Fire' class by comparing
the U-Net architecture with Attention U-Net and Trans U-Net in order to choose the best
performing model.
The EfficientNetB2 architecture for classification gave satisfactory results with an accuracy of
71.72%. Concerning segmentation, we adopted the U-Net model which offers a segmentation
accuracy that reaches 98%. As for the deployment, a fire detection application was designed
using Android Studio software by assimilating the drone's camera.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
Automatic detection Forest fires Deep Learning Convolutional neural networks Classification Semantic segmentation Xception EfficientNetB2 U-Net Attention U-Net Trans U-Net Deployment