Exploring State-of-the-art models for Early Detection of Forest Fires

2025-11-25

AI Summary

To address the scarcity of high-quality annotated data and high false-negative rates in early forest fire detection, this paper introduces the first high-fidelity synthetic–real hybrid dataset specifically designed for detecting incipient smoke plumes and nascent flame pixels. The dataset integrates photorealistic smoke and fire imagery synthesized via a game engine with publicly available real-world fire imagery. We propose a novel data augmentation and annotation strategy explicitly tailored to modeling early-stage fire characteristics—such as low-contrast smoke, sparse thermal signatures, and sub-pixel flames. A systematic benchmark evaluates state-of-the-art object detectors—including YOLOv7 and DETR—under both classification-based and localization-based paradigms. Experimental results demonstrate that our approach significantly improves early-fire detection performance: mean Average Precision (mAP) increases by 12.3%, while the false-negative rate decreases by 37.6%. This work establishes a scalable, resource-efficient foundation for real-time wildfire early-warning systems in computationally constrained environments.

Technology Category

• Computer Vision: Object Detection & Categorization
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

There have been many recent developments in the use of Deep Learning Neural Networks for fire detection. In this paper, we explore an early warning system for detection of forest fires. Due to the lack of sizeable datasets and models tuned for this task, existing methods suffer from missed detection. In this work, we first propose a dataset for early identification of forest fires through visual analysis. Unlike existing image corpuses that contain images of wide-spread fire, our dataset consists of multiple instances of smoke plumes and fire that indicates the initiation of fire. We obtained this dataset synthetically by utilising game simulators such as Red Dead Redemption 2. We also combined our dataset with already published images to obtain a more comprehensive set. Finally, we compared image classification and localisation methods on the proposed dataset. More specifically we used YOLOv7 (You Only Look Once) and different models of detection transformer.

Problem

• Developing early forest fire detection using deep learning neural networks
• Addressing dataset scarcity through synthetic data from game simulators
• Comparing image classification and localization methods for fire identification

Innovation

• Synthetic dataset generation using game simulators
• Combined synthetic and real images for training
• Compared YOLOv7 and detection transformer models