Fire Detection Systems Using Deep Learning Approaches

The advent of deep learning has transformed fire detection, enabling rapid and accurate identification of flames and smoke across diverse environments. Convolutional neural networks (CNNs) lie at the heart of most current systems, learning discriminative visual features directly from images or video frames. Object detection frameworks perform end-to-end identification of fire regions, while spatiotemporal models capture the dynamic evolution of flames and smoke. Attention mechanisms and feature pyramids further refine performance by emphasising salient regions and integrating information at multiple scales. These advances have driven applications ranging from early forest fire warning via satellite and unmanned aerial vehicle imagery to real-time surveillance in urban and indoor settings. Challenges remain in managing small targets, varying illumination, occlusion by natural elements and minimising false alarms caused by fire-like textures. Nonetheless, deep learning approaches continue to improve detection speed and robustness, offering global significance in climate resilience, disaster mitigation and public safety.

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Fire Detection Systems Using Deep Learning Approaches publication trend

The graph below shows the total number of articles in fire detection systems using deep learning approaches across all publications each year (not limited to Nature Index journals).

Technical terms

Convolutional Neural Network (CNN): A class of deep learning model that extracts hierarchical spatial features from images by applying learnable filters.

You Only Look Once (YOLO): A real-time object detection framework that predicts bounding boxes and class probabilities in a single network pass.

Long Short-Term Memory (LSTM): A recurrent neural network architecture designed to capture long-range temporal dependencies in sequential data.

Attention Mechanism: A method that dynamically weights input features or time steps to highlight the most relevant information for a given task.

Spatiotemporal Features: Combined spatial and temporal representations that describe how visual patterns evolve over time in video data.

References

1. Fire-YOLO: A Small Target Object Detection Method for Fire Inspection. Sustainability (2022).
2. An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems. Sensors (2021).
3. An Attention Enhanced Bidirectional LSTM for Early Forest Fire Smoke Recognition. IEEE Access (2019).