A lightweight deep learning approach for fire detection in highway tunnels: Enhancing real-time capability and reliability

Highway tunnels are critical transport assets in which fires can cause cascading failures, mass casualties, and long service interruption. Traditional point-sensor and video-based systems frequently fail to provide timely early warnings, as they lack the sensitivity required to detect incipient fires under the complex lighting and visibility constraints of urban tunnels. This study develops a real-time vision-based warning module using two lightweight convolutional neural networks trained on a mixed dataset consisting of 281 Internet tunnel-fire images, 1552 open-source vehicle-fire images, and 592 small-scale tunnel-fire experimental images collected in collaboration with Jiandun Fire Technology Co., Ltd. The dataset is divided into training, validation, and testing subsets at an approximately 8:1:1 ratio, and the positive test samples are restricted to tunnel-fire images to evaluate tunnel-specific generalization. The lightweight models are compared with deeper CNN baselines in terms of both predictive performance and computational cost. On the tunnel-focused test set, the best model achieves 98.75% accuracy, 98.0% recall, and 8.24 ms latency on a Tesla T4 GPU, indicating that real-time deployment is feasible without substantial loss of reliability. Robustness tests with Gaussian and salt-and-pepper noise quantify sensitivity to degraded imagery and highlight the practical value of denoising and image-quality control. Grad-CAM visualizations further show that the model attends to physically meaningful flame and smoke regions, supporting interpretability and engineering acceptance in tunnel safety applications.