Abstract
To minimize the damage caused by wildfires, a deep learning-based wildfire-detection technology that extracts features and patterns from surveillance camera images was developed. However, many studies related to wildfire-image classification based on deep learning have highlighted the problem of data imbalance between wildfire-image data and forest-image data. This data imbalance causes model performance degradation. In this study, wildfire images were generated using a cycle-consistent generative adversarial network (CycleGAN) to eliminate data imbalances. In addition, a densely-connected-convolutional-networks-based (DenseNet-based) framework was proposed and its performance was compared with pre-trained models. While training with a train set containing an image generated by a GAN in the proposed DenseNet-based model, the best performance result value was realized among the models with an accuracy of 98.27% and an F1 score of 98.16, obtained using the test dataset. Finally, this trained model was applied to high-quality drone images of wildfires. The experimental results showed that the proposed framework demonstrated high wildfire-detection accuracy.
Highlights
Wildfires cause significant harm to humans and damage to private and public property; they pose a constant threat to public safety
Wildfires were primarily detected by human observers, but a deep learning-based automatic wildfire detection system with real-time surveillance cameras has the advantage of the possibility of constant and accurate monitoring, compared to human observers
As noted by Jain et al in their review of machine-learning applications in wildfire detection [23], Zhang et al found that convolutional neural networks (CNNs) outperforms the support vector machines (SVM)-based method [24], and Cao et al reported a 97.8% accuracy rate for smoke detection, using convolutional layers [25]
Summary
Wildfires cause significant harm to humans and damage to private and public property; they pose a constant threat to public safety. With the rapid development of digital-cameras and image-processing technologies, traditional methods are replaced by video- and image-data-based methods [8] Using these methods, a large area of a forest can be monitored, where fires and smoke can be detected immediately after the outbreak of a wildfire. The early wildfire-detection algorithm was constructed using the state-of-the-art net architecture, DenseNet, which is known to perform well in wildfire detection, while alleviating the vanishing gradient problem and reducing the training time [40].
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