Edge-AI Enabled Real-Time Forest Fire Detection and Early Warning Framework Using YOLOv8 and IoT Technologies
YOLOv8とIoT技術を用いたエッジAI対応リアルタイム森林火災検知・早期警報フレームワーク (AI 翻訳)
Shashikala T K, S. R, J. Chandrashekhara
🤖 gxceed AI 要約
日本語
本論文は、YOLOv8とIoTセンサーを用いたエッジAIベースの森林火災検知・早期警報フレームワークを提案する。エッジデバイス上でリアルタイムに火災・煙を検知し、センサーフュージョンで誤警報を低減する。低遅延・高精度・スケーラブルな監視を実現し、気候変動適応に貢献する。
English
This paper proposes an edge-AI framework for real-time forest fire detection using YOLOv8 and IoT sensors. It enables low-latency, accurate detection with sensor fusion to reduce false alarms, offering scalable deployment for climate adaptation.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、気候変動による森林火災リスク増大が懸念されており、自治体や森林管理機関にとって早期警報システムの導入は適応策として重要。ただし、GX開示や脱炭素政策との直接的な関連は薄い。
In the global GX context
Globally, forest fires are increasingly linked to climate change, making early detection critical for adaptation. This framework offers a scalable solution for disaster management, relevant to climate resilience strategies.
👥 読者別の含意
🔬研究者:AIとIoTを組み合わせた気候適応技術の実装例として参考になる。
🏢実務担当者:森林管理や防災担当者は、リアルタイム監視システムの導入検討に活用できる。
🏛政策担当者:気候変動適応政策における防災インフラ強化の一環として注目すべき。
📄 Abstract（原文）
Forest fires are among the most destructive natural disasters, causing significant environmental damage, biodiversity loss, economic disruption, and threats to human life. Conventional fire monitoring techniques, such as watchtowers, satellite imaging, and manual patrols, often suffer from delayed detection, limited coverage, and high operational costs. This paper proposes an Edge-AI Enabled Real-Time Forest Fire Detection and Early Warning Framework using the YOLOv8 object detection model and Internet of Things (IoT) technologies. The proposed framework performs real-time fire and smoke detection directly on edge devices, reducing detection latency, bandwidth consumption, and dependence on cloud connectivity, making it suitable for remote forest environments. Environmental data collected from IoT sensors, including temperature, humidity, smoke concentration, carbon monoxide (CO), and air quality, are integrated through a sensor fusion mechanism to validate visual detections and minimize false alarms. The framework comprises five layers: Data Acquisition, Edge AI Processing, IoT Sensing, Cloud Communication, and Emergency Alert Generation. Detection results, GPS location, confidence score, timestamp, and sensor readings are transmitted to a cloud dashboard, where automated alerts are delivered to forest authorities via SMS, email, mobile applications, or web platforms. The proposed framework provides low-latency processing, improved detection accuracy, reliable early warning, and scalable deployment for intelligent wildfire monitoring. Future work includes integrating thermal imaging, Vision Transformers, Explainable AI, federated learning, autonomous drones, and Digital Twin technology to further enhance wildfire prediction and disaster management.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://aijfr.com/papers/2026/4/6951.pdffirst seen 2026-08-09 05:53:30
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