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

• Author(s):

  Mrs.B.Sathya | Harihasudhanks | Karthik Ramanathan Sr | Madhushudanan V

• 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.

• 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..

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Other Details

• Paper id:

  IJSARTV12I5105473

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-24

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