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 models 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 frame rate increase from 46 fps to 59 fps compared to the original YOLOv5s,making the method more practical.