Lightweight Deep Learning Model for Fire Classification in Tunnels

Tunnel fires pose a severe threat to human safety and infrastructure, necessitating the development of advanced and efficient fire detection systems. This paper presents a novel lightweight deep learning (DL) model specifically designed for real-time fire classification in tunnel environments. This model integrates MobileNetV3 for spatial feature extraction, Temporal Convolutional Networks (TCNs) for temporal sequence analysis, and advanced attention mechanisms, including Convolutional Block Attention Modules (CBAMs) and Squeeze-and-Excitation (SE) blocks, to prioritize critical features such as flames and smoke patterns while suppressing irrelevant noise. The model is trained on a custom dataset containing real tunnel fire incidents generated using a newly prepared dataset. This approach enhances the model generalization capabilities, enabling it to handle diverse fire scenarios, including those with low visibility, high smoke density, and variable ventilation conditions. Deployment optimizations, such as quantization and layer fusion, ensure computational efficiency, achieving an average inference time of 12ms/frame, making it suitable for resource-constrained environments like IoT and edge devices. The experimental results demonstrate that the proposed model achieves an accuracy of 96.5%, a precision of 95.7%, and a recall of 97.2%, significantly outperforming state-of-the-art (SOTA) models such as ResNet50 and YOLOv5 in both accuracy and real-time performance. Robustness tests under challenging conditions validate model reliability and adaptability, marking it as a critical advancement in tunnel fire detection systems. This study provides valuable insights into the design and deployment of efficient fire classification systems for safety-critical applications. The proposed model offers a scalable, high-performance solution for tunnel fire monitoring and establishes a benchmark for future research in real-time video-based classification under complex environmental conditions.