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

In the charging process of electric vehicle (EV), 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 smoke detection...

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Bibliographic Details
Published in:Journal of real-time image processing Vol. 20; no. 2; p. 38
Main Authors: Gao, Dexin, Zhang, Shiyu, Ju, Yifan, Yang, Qing
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Cellular telephones
Classification
Clustering
Computer Graphics
Computer networks
Computer Science
Datasets
Electric vehicle charging
Electric vehicle charging stations
Emergency procedures
Fire detection
Image Processing and Computer Vision
Machine vision
Monitoring
Multimedia Information Systems
Network management systems
Neural networks
Optimization
Pattern Recognition
Real time
Signal,Image and Speech Processing
Smoke
Tracking
Vision systems
Kmeans +  + clustering algorithm
Fire smoke detection
EV charging station
CA module
GhostNet-YOLOv4-CA
ISSN:1861-8200, 1861-8219
Online Access:Get full text
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Summary:In the charging process of electric vehicle (EV), 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 smoke detection method for EV charging station based on Machine Vision. The algorithm introduces the Kmeans +  + algorithm in the GhostNet-YOLOv4 model to rescreen anchor boxes for fire smoke targets to optimize the classification quality for the complex and variable features of 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 11.436 M, a mAP value of 87.70%, 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 EV charging station and the emergency extinguishing of fire.
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ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-023-01293-9