A real-time fire and flame detection method for electric vehicle charging station based on Machine Vision

Abstract

Abstract In the charging process of electric vehicles (EVs), high voltage and high current charging methods are widely used to reduce charging time, resulting in severe battery heating and an increased risk of fire. To improve fire detection efficiency, this paper proposes a real-time fire and flame detection method for electric vehicle charging station based on Machine Vision. The algorithm introduces the Kmeans + + algorithm in the GhostNet-YOLOv4 model to rescreen anchor boxes for flame smoke targets to optimize the classification quality for the complex and variable features of flame smoke targets; and introduces the coordinate attention (CA) module after the lightweight backbone network GhostNet to improve the classification quality. In this paper, we use EV charging station monitoring video as a model detection input source to achieve real-time detection of multiple pairs of sites. The experimental results demonstrate that the improved algorithm has a model parameter number of 11.436M, a mAP value of 87.70 percent, and a video detection FPS value of 75, which has a good continuous target tracking capability and satisfies the demand for real-time monitoring and is crucial for the safe operation of electric vehicle charging stations and the emergency extinguishing of fires.