Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques
This study presents FireNet-CNN, an advanced deep-learning model particularly designed for forest fire detection, which significantly surpasses existing methods in terms of reliability, efficiency, and interpretability. FireNet-CNN is compared to popular pre-trained models, including VGG16, VGG19, a...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10930496/ |
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| author | Gazi Mohammad Imdadul Alam Naima Tasnia Tapu Biswas Md. Jakir Hossen Sharia Arfin Tanim Md Saef Ullah Miah |
| author_facet | Gazi Mohammad Imdadul Alam Naima Tasnia Tapu Biswas Md. Jakir Hossen Sharia Arfin Tanim Md Saef Ullah Miah |
| author_sort | Gazi Mohammad Imdadul Alam |
| collection | DOAJ |
| description | This study presents FireNet-CNN, an advanced deep-learning model particularly designed for forest fire detection, which significantly surpasses existing methods in terms of reliability, efficiency, and interpretability. FireNet-CNN is compared to popular pre-trained models, including VGG16, VGG19, and Inception V3, across key performance metrics and consistently shows superior results, achieving 99.05% accuracy, 99.41% precision, and 98.28% recall. The model was evaluated using two augmented datasets: Dataset A and Dataset B, which consist of fire and non-fire images sourced from multiple video and image datasets. FireNet-CNN’s architecture, which includes 2.75 million parameters and a compact model size of 10.58 MB, has been meticulously optimized for fire detection tasks. As a consequence, the inference time of 0.95 seconds/image enables fast real-time deployment especially suitable for resource-constrained platforms like drones, remote sensors or other types of embedded systems in wooded regions. FireNet-CNN uses synthetic data augmentation based on Stable Diffusion to overcome the limitations of dataset size and class imbalance. This augmentation is critical as it helps the model accurately identify fire instances with a lower false positive rate, which is key for any real-time fire detection system where reliability and dependability are vital. To improve transparency and trust in safety-critical applications, FireNet-CNN incorporates the explainable AI (XAI) techniques, such as Grad-CAM and Saliency Maps. Despite encountering challenges such as reliance on synthetic data and issues of class imbalance, FireNet-CNN has demonstrated promising potential as a viable and effective solution for early wildfire detection. It offers significant insights for future research and practical applications in fire management and disaster response. |
| format | Article |
| id | doaj-art-838206da7ebf4eecac629d010b590770 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-838206da7ebf4eecac629d010b5907702025-08-20T02:12:29ZengIEEEIEEE Access2169-35362025-01-0113511505118110.1109/ACCESS.2025.355235210930496Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI TechniquesGazi Mohammad Imdadul Alam0https://orcid.org/0009-0001-7636-990XNaima Tasnia1https://orcid.org/0009-0005-6562-1056Tapu Biswas2https://orcid.org/0009-0005-5267-8424Md. Jakir Hossen3https://orcid.org/0000-0002-9978-7987Sharia Arfin Tanim4https://orcid.org/0009-0009-5231-3432Md Saef Ullah Miah5https://orcid.org/0000-0003-4587-4636School of Science, Engineering and Technology, East Delta University, Chittagong, BangladeshSchool of Science, Engineering and Technology, East Delta University, Chittagong, BangladeshSchool of Mathematics, Statistics and Actuarial Science, University of Essex, Colchester, U.K.Faculty of Engineering and Technology (FET), Multimedia University, Malacca, MalaysiaDepartment of Computer Science, American International University-Bangladesh, Dhaka, BangladeshFaculty of Engineering and Technology (FET), Multimedia University, Malacca, MalaysiaThis study presents FireNet-CNN, an advanced deep-learning model particularly designed for forest fire detection, which significantly surpasses existing methods in terms of reliability, efficiency, and interpretability. FireNet-CNN is compared to popular pre-trained models, including VGG16, VGG19, and Inception V3, across key performance metrics and consistently shows superior results, achieving 99.05% accuracy, 99.41% precision, and 98.28% recall. The model was evaluated using two augmented datasets: Dataset A and Dataset B, which consist of fire and non-fire images sourced from multiple video and image datasets. FireNet-CNN’s architecture, which includes 2.75 million parameters and a compact model size of 10.58 MB, has been meticulously optimized for fire detection tasks. As a consequence, the inference time of 0.95 seconds/image enables fast real-time deployment especially suitable for resource-constrained platforms like drones, remote sensors or other types of embedded systems in wooded regions. FireNet-CNN uses synthetic data augmentation based on Stable Diffusion to overcome the limitations of dataset size and class imbalance. This augmentation is critical as it helps the model accurately identify fire instances with a lower false positive rate, which is key for any real-time fire detection system where reliability and dependability are vital. To improve transparency and trust in safety-critical applications, FireNet-CNN incorporates the explainable AI (XAI) techniques, such as Grad-CAM and Saliency Maps. Despite encountering challenges such as reliance on synthetic data and issues of class imbalance, FireNet-CNN has demonstrated promising potential as a viable and effective solution for early wildfire detection. It offers significant insights for future research and practical applications in fire management and disaster response.https://ieeexplore.ieee.org/document/10930496/Forest fire detectiondeep learninggenerative AIexplainable AIwildfire monitoring |
| spellingShingle | Gazi Mohammad Imdadul Alam Naima Tasnia Tapu Biswas Md. Jakir Hossen Sharia Arfin Tanim Md Saef Ullah Miah Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques IEEE Access Forest fire detection deep learning generative AI explainable AI wildfire monitoring |
| title | Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques |
| title_full | Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques |
| title_fullStr | Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques |
| title_full_unstemmed | Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques |
| title_short | Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques |
| title_sort | real time detection of forest fires using firenet cnn and explainable ai techniques |
| topic | Forest fire detection deep learning generative AI explainable AI wildfire monitoring |
| url | https://ieeexplore.ieee.org/document/10930496/ |
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