Deep Learning-Based Wildfire Image Detection and Classification Systems for Controlling Biomass

• Published in: International journal of intelligent systems 2023-09, Vol.2023, p.1-18
• Main Authors: Prakash, M., Neelakandan, S., Tamilselvi, M., Velmurugan, S., Baghavathi Priya, S., Ofori Martinson, Eric
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Biomass; Biomass burning; Blurring; Classification; Climate change; Critical field (superconductivity); Datasets; Deep learning; Forest & brush fires; Forest fire detection; Forest fires; Image classification; Image detection; Image enhancement; Intelligent systems; Internet of Things; Localization; Machine learning; Natural resources; Neural networks; Noise reduction; Optimization; Optimization techniques; Radial basis function; Restoration; Warning systems; Water waves
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1155/2023/7939516

Forests are essential natural resources that directly impact the ecosystem. However, the rising frequency of forest fires due to natural and artificial climate change has become a critical issue. A revolutionary municipal application proposes deploying an artificial intelligence-based forest fire warning system to prevent major disasters. This work aims to present an overview of vision-based methods for detecting and categorizing forest fires. The study employs a forest fire detection dataset to address the classification difficulty of discriminating between photos with and without fire. This method is based on convolutional neural network transfer learning with Inception-v3. Thus, automatic identification of current forest fires (including burning biomass) is a critical field of research for reducing negative repercussions. Early fire detection can also assist decision-makers in developing mitigation and extinguishment strategies. Radial basis function Networks (RBFNs) with rapid and accurate image super resolution (RAISR) is a deep learning framework trained on an input dataset to detect active fires and burning biomass. The proposed RBFN-RAISR model’s performance in recognizing fires and nonfires was compared to earlier CNN models using several performance criteria. The water wave optimization technique is used for image feature selection, noise and blurring reduction, image improvement and restoration, and image enhancement and restoration. When classifying fire and no-fire photos, the proposed RBFN-RAISR fire detection approach achieves 97.55% accuracy, 93.33% F-Score, 96.44% recall, 94.19% precision, and an error rate of 24.89. Given the one-of-a-kind forest fire detection dataset, the suggested method achieves promising results for the forest fire categorization problem.