An innovative hybrid method utilizing fused transformer-based deep features and deep neural networks for detecting forest fires

Forest fires, one of the most pernicious and devastating disasters, cause deforestation, wildlife extinction, global warming, and climate change. Early fire detection is critical before it reaches catastrophic dimensions. Artificial intelligence-based systems that detect forest fires accurately and quickly are needed for early intervention. Delayed extinguishing efforts without such systems cause tremendous damage and losses. An effective monitoring system allows for the reduction of fire damage and hence prevents forest loss. This study aims to develop a successful artificial intelligence model to detect fire from forest landscape images. In this context, this work is new as it provides new insights into robust and scientific modeling for forest fire detection by analyzing feature maps based on fused transformer architectures using Deep Neural Networks. The experimental models were validated using accuracy, sensitivity, precision, and area under the receiver operating characteristic curve measures. The validation findings reveal that the proposed hybrid model performs the best while all models yield reasonable results. To summarize, satisfactory accuracy values of 99.58% and 96.79% for both datasets, respectively, strongly support the proposed hybrid model’s fire detection achievement with the 5-fold cross-validation. Furthermore, the high sensitivity and high precision measures imply that the model has few false negatives and false positives. Considering the obtained accuracies, the proposed hybrid model could be used for comprehensive fire detection modeling. To the author’s best knowledge, this study is the first to use transformer architectures and Deep Neural Networks for forest fire detection and is therefore important for the relevant literature. In this context, this study presents a new approach to distinguishing landscape images of forest fires and further developing fire detection strategies by the role of transformer architectures in feature extraction. It is thought that by executing the proposed model in an unmanned aerial vehicle equipped with a real-time system, fire detection will provide decision support to field professionals in reducing damage and managing forest fires.