Article
Deep Learning–Driven Object Detection for Wildfire Management and Early Intervention
The rapid increase in global wildfires, driven by climate change, demands a transition from reactive suppression to proactive, intelligence-based detection systems. Traditional wildfire monitoring methods, such as human surveillance and satellite remote sensing, often suffer from high latency, high operational costs, and limited temporal resolution. A major challenge in current automated systems is the "false alarm" problem, where modern Convolutional Neural Networks (CNNs), including You Only Look Once (YOLO), misclassify non-fire elements like sunsets, industrial glare, or red-colored objects as fire. These inaccuracies lead to unnecessary emergency responses and increased alert fatigue among authorities. To address these limitations, this research proposes VLM-FireNet, a hybrid cascade architecture that combines the speed of edge computing with advanced contextual reasoning. The system utilizes YOLOv8 for rapid initial detection at the edge, achieving inference times below 50 milliseconds. Detected instances are then validated using a Transformer-based Vision-Language Model (VLM), which applies a global self-attention mechanism to analyze the broader scene context and eliminate false positives. This dual-check framework significantly enhances detection reliability. The system is implemented using a multithreaded Python environment, integrating a local Tkinterbased interface with a remote Telegram Bot API for real-time alert notifications. The proposed approach improves detection accuracy while maintaining real-time performance. By reducing false positives by approximately 20%, VLM-FireNet provides a scalable and cost-effective solution for smart city and forest monitoring, contributing to more efficient and reliable disaster management systems.
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