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

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 5; p. 1428
• Main Authors: Vorwerk, Pascal, Kelleter, Jörg, Müller, Steffen, Krause, Ulrich
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.02.2024; MDPI
• Subjects: Classification; early fire detection; electronic nose; Experiments; feature fusion; gas sensors; Historic buildings; Humidity; Laboratories; multi-sensor nodes; Sensors; transfer learning; VOCs; Volatile organic compounds; Germany; multi-sensor nodes; transfer learning; feature fusion; gas sensors; early fire detection; electronic nose; linear discriminant analysis (LDA); classification
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24051428

Effective early fire detection is crucial for preventing damage to people and buildings, especially in fire-prone historic structures. However, due to the infrequent occurrence of fire events throughout a building's lifespan, real-world data for training models are often sparse. In this study, we applied feature representation transfer and instance transfer in the context of early fire detection using multi-sensor nodes. The goal was to investigate whether training data from a small-scale setup (source domain) can be used to identify various incipient fire scenarios in their early stages within a full-scale test room (target domain). In a first step, we employed Linear Discriminant Analysis (LDA) to create a new feature space solely based on the source domain data and predicted four different fire types (smoldering wood, smoldering cotton, smoldering cable and candle fire) in the target domain with a classification rate up to 69% and a Cohen's Kappa of 0.58. Notably, lower classification performance was observed for sensor node positions close to the wall in the full-scale test room. In a second experiment, we applied the TrAdaBoost algorithm as a common instance transfer technique to adapt the model to the target domain, assuming that sparse information from the target domain is available. Boosting the data from 1% to 30% was utilized for individual sensor node positions in the target domain to adapt the model to the target domain. We found that additional boosting improved the classification performance (average classification rate of 73% and an average Cohen's Kappa of 0.63). However, it was noted that excessively boosting the data could lead to overfitting to a specific sensor node position in the target domain, resulting in a reduction in the overall classification performance.