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Classification in Early Fire Detection Using Multi-Sensor Nodes-A Transfer Learning Approach.

Pascal Vorwerk¹, Jörg Kelleter², Steffen Müller²

¹Faculty of Process- and Systems Engineering, Institute of Apparatus and Environmental Technology, Otto von Guericke University of Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.

Sensors (Basel, Switzerland)

|March 13, 2024

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Transfer learning effectively detects early fires using multi-sensor data. Training models on small-scale data improved detection in a full-scale room, enhancing safety for historic structures.

Area of Science:

Engineering
Computer Science
Fire Safety

Background:

Early fire detection is critical for protecting lives and property, especially in vulnerable historic buildings.
Sparse real-world data for training fire detection models is a significant challenge due to infrequent fire events.

Purpose of the Study:

To investigate the transferability of early fire detection models trained on small-scale data to a full-scale environment.
To evaluate feature representation transfer and instance transfer techniques for multi-sensor fire detection.

Main Methods:

Linear Discriminant Analysis (LDA) was used for feature space transformation on source domain data.
TrAdaBoost algorithm was applied for instance transfer, adapting models with sparse target domain data.
Classification performance was evaluated for four fire types across different sensor node positions.

Keywords:

classification early fire detection electronic nose feature fusion gas sensors linear discriminant analysis (LDA)multi-sensor nodes transfer learning

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Main Results:

LDA achieved up to 69% classification rate and Cohen's Kappa of 0.58 in the full-scale room.
TrAdaBoost improved average classification to 73% and Cohen's Kappa to 0.63 with targeted data boosting.
Sensor nodes near walls showed lower classification performance; excessive boosting led to overfitting.

Conclusions:

Feature and instance transfer learning are viable for early fire detection using multi-sensor data.
Transfer learning can bridge the gap between limited training data and real-world application, enhancing fire safety systems.
Careful application of instance transfer is necessary to avoid overfitting and maintain generalizability.