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The detection of tunnel fire presents challenges in terms of detection difficulty and the difficulty of deploying it directly on resource-limited embedded devices for real-time detection. In this paper, a tunnel fire detection algorithm based on an improved YOLOv8 is proposed. Firstly, a polarized attention mechanism is introduced to preserve high-resolution information and suppress redundant features while enhancing the capture of global information. Secondly, a novel Partial Convolution (PConv) is introduced to achieve low latency and high throughput in the model. Lastly, the WIoU (Weighted Intersection over Union) function is used to optimize the network"s bounding box loss, enabling fast convergence of the network. Experimental results demonstrate that the proposed network achieves a 1.3% improvement in mean Average Precision (mAP) on the utilized tunnel fire dataset. Furthermore, the lightweight model reduces the model parameters by 29.7%, and the forward inference time is reduced by 44%. The algorithm achieves a balance between accuracy and lightweight requirements, making it suitable for real-time detection in tunnel scenarios.