Publicação
Deep Learning approach applied to drone imagery for the automatic detection of forest fire
| Resumo: | 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. |
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| Autores principais: | Akhatar, Maryeme |
| Assunto: | Automatic detection Forest fires Deep Learning Convolutional neural networks Classification Semantic segmentation Xception EfficientNetB2 U-Net Attention U-Net Trans U-Net Deployment |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | 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. |
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