In response to the current problems of low detection accuracy and slow detection speed of vehicle fires in charging stations, this paper proposes a vehicle fire detection method YOLOv5s-Fast based on YOLOv5s improvement from a practical perspective. This article first uses the global context attention mechanism and C3 module to fuse in the Backbone network, becoming a new feature extraction module C3GC, enhancing the model's ability to extract features and reducing computational complexity. Secondly, in the Neck network, this paper adopts a lightweight upsampling operator that can adaptively upsample based on the input image, improving detection accuracy. Finally, this article introduces a decoupling head to improve the accuracy and efficiency of object detection. The experimental results show that the proposed method YOLOv5-Fast has an average accuracy improvement of 4.9% and an FPS increase from 46 to 59 compared to the original YOLOv5s, making the method more practical.