Lightweight Fire Detection in Tunnel Environments

Tunnel fires pose significant challenges to public safety due to their rapid development and the confined nature of tunnel environments. Traditional fire detection systems often struggle with delayed response times and high false alarm rates, particularly in complex scenarios. This study proposes a lightweight hybrid deep learning (DL) model that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal analysis, offering an efficient and robust solution for real-time tunnel fire detection. Leveraging transfer learning, the model adapts to tunnel-specific fire scenarios with minimal training data, significantly improving its generalization capabilities. The lightweight architecture ensures computational efficiency, making it suitable for deployment in resource-constrained environments such as tunnels with limited processing capacity. The model was rigorously evaluated on datasets combining simulated and real-world fire scenarios. It achieved an accuracy of 92%, a precision of 89%, a recall of 90%, and an F1 score of 89.5%, outperforming state-of-the-art (SOTA) models in all key metrics. Furthermore, the model demonstrated resilience under varied environmental conditions, including high smoke density and sensor failures, maintaining reliable performance. This study highlights the potential of lightweight deep learning models in enhancing tunnel safety systems by providing accurate, fast, and dependable fire and smoke detection. Future work will extend the methodology to other critical infrastructures and optimize the model for broader applications.