FIRE-YOLOv8s: A Lightweight and Efficient Algorithm for Tunnel Fire Detection

To address the challenges of high algorithmic complexity and low accuracy in current fire detection algorithms for highway tunnel scenarios, this paper proposes a lightweight tunnel fire detection algorithm, FIRE-YOLOv8s. First, a novel feature extraction module, P-C2f, is designed using partial convolution (PConv). By dynamically determining the convolution kernel's range of action, the module significantly reduces the model's computational load and parameter count. Additionally, the ADown module is introduced for downsampling, employing a lightweight and branching design to minimize computational requirements while preserving essential feature information. Secondly, the neck feature fusion network is redesigned using a lightweight CNN-based cross-scale fusion module (CCFF). This module leverages lightweight convolution operations to achieve efficient cross-scale feature fusion, further reducing model complexity and enhancing the fusion efficiency of multi-scale features. Finally, the dynamic head detection head is introduced, incorporating multiple self-attention mechanisms to better capture key information in complex scenes. This improvement enhances the model's accuracy and robustness in detecting fire targets under challenging conditions. Experimental results on the self-constructed tunnel fire dataset demonstrate that, compared to the baseline model YOLOv8s, FIRE-YOLOv8s reduces the computational load by 47.2%, decreases the number of parameters by 52.2%, and reduces the model size to 50% of the original, while achieving a 4.8% improvement in accuracy and a 1.7% increase in mAP@0.5. Furthermore, deployment experiments on a tunnel emergency firefighting robot platform validate the algorithm's practical applicability, confirming its effectiveness in real-world scenarios.