Forest Fire Detection System Using DL

Abstract: This Forest fires represent a catastrophic threat to global biodiversity and ecological stability, necessitating the development of high-precision, real-time early warning systems. This project introduces a comprehensive monitoring framework that leverages Google’s SigLIP (Sigmoid Loss for Language Image Pre-training), a state-of- the-art Vision Transformer (ViT) architecture, to detect fire and smoke anomalies in various environmental contexts. Unlike traditional Convolutional Neural Networks (CNNs), the implemented SigLIP model utilizes global attention mechanisms to effectively distinguish between subtle visual cues, such as differentiating early-stage smoke from clouds, fog, or thermal haze.The system was fine-tuned on a diverse dataset comprising thousands of images from sources including the FLAME and DeepFire datasets, supplemented by synthetic data for edge-case training. The technical architecture is deployed through a dual-platform approach: a rapid-response Streamlit interface for interactive testing and a full-scale Flask web portal. The Flask-based application provides a production-ready environment featuring secure user authentication, an administrative dashboard for detection logging, and integrated email notification triggers via EmailJS for immediate alert dissemination.Functionally, the application supports both high-resolution static image analysis and sampled video stream processing (MP4/AVI). By utilizing confidence-based thresholding and multi-class probability mapping (Normal, Smoke, and Fire), the system provides actionable intelligence for satellite monitoring, drone surveillance, and fixed CCTV footage. The resulting solution offers a scalable, high-accuracy tool for environmental protection agencies to mitigate devastating impacts of wildfires through rapid, AI-driven detection.

Keywords: Forest Fire Detection, Deep Learning, Computer Vision, Vision Transformer (ViT), SigLIP, Smoke Detection, Fire Detection, Real-time Monitoring, Early Warning System,Transfer Learning, Flask, Streamlit,FLAME Dataset, DeepFire Dataset, Image Classification, Drone Surveillance, Environmental Monitoring.