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Volume 12, Issue 5 (May 2026)

Real-time Detection Of Forest Fires Using Firenet-cnn And Explainable Ai Techniques

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Volume 12 Issue 07

July 2026

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Author(s)

Mrs.B.Sathya Harihasudhanks Karthik Ramanathan Sr Madhushudanan V

Abstract

This Study Proposes FireNet-CNN, A Lightweight And Efficient Deep Learning Model For Real-time Forest Fire Detection Using Convolutional Neural Networks (CNN) And Explainable AI (XAI) Techniques. The Model Was Trained And Evaluated On Augmented Datasets Containing Fire And Non-fire Images, Achieving High Performance With 99.05% Accuracy, 99.41% Precision, And 98.28% Recall. Stable Diffusion-based Synthetic Image Generation And Traditional Augmentation Methods Were Used To Improve Dataset Diversity And Reduce Class Imbalance. To Enhance Transparency And Reliability, Grad-CAM And Saliency Map Techniques Were Integrated To Visualize The Model’s Decision-making Process By Highlighting Fire-related Regions In Images. With A Compact Model Size And Fast Inference Time, FireNet-CNN Is Suitable For Deployment In Real-time Wildfire Monitoring Systems, Drones, And Embedded Devices For Early Forest Fire Detection And Disaster Management..

Keywords

Forest Fire Detection FireNet-CNN Deep Learning Convolutional Neural Network (CNN) Explainable AI (XAI) Grad-CAM Saliency Map Stable Diffusion Data Augmentation Wildfire Monitoring Image Classification Real-time Detection.

Paper ID

IJSARTV12I5105473

Publication Date

May 24, 2026

Research Area

Deep Learning-based Forest Fire Detection Using Explainable AI

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