Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring
In the process of charging and using electric vehicles, lithium battery may cause hazards such as fire or even explosion due to thermal runaway. Therefore, a target detection model based on the improved YOLOv5 (You Only Look Once) algorithm is proposed for the features generated by lithium battery c...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/9215528 |
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| _version_ | 1850178366284496896 |
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| author | Yuchen Gao Qing Yang Shiyu Zhang Dexin Gao |
| author_facet | Yuchen Gao Qing Yang Shiyu Zhang Dexin Gao |
| author_sort | Yuchen Gao |
| collection | DOAJ |
| description | In the process of charging and using electric vehicles, lithium battery may cause hazards such as fire or even explosion due to thermal runaway. Therefore, a target detection model based on the improved YOLOv5 (You Only Look Once) algorithm is proposed for the features generated by lithium battery combustion, using the K-means algorithm to cluster and analyse the target locations within the dataset, while adjusting the residual structure and the number of convolutional kernels in the network and embedding a convolutional block attention module (CBAM) to improve the detection accuracy without affecting the detection speed. The experimental results show that the improved algorithm has an overall mAP evaluation index of 94.09%, an average F1 value of 90.00%, and a real-time detection FPS (frames per second) of 42.09, which can meet certain real-time monitoring requirements and can be deployed in various electric vehicle charging stations and production platforms for safety detection and will provide a guarantee for the safe production and development of electric vehicles in the future. |
| format | Article |
| id | doaj-art-c485766ff96d4f0cba7d1004e26c3868 |
| institution | OA Journals |
| issn | 2090-0155 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-c485766ff96d4f0cba7d1004e26c38682025-08-20T02:18:46ZengWileyJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/9215528Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety MonitoringYuchen Gao0Qing Yang1Shiyu Zhang2Dexin Gao3School of Information Science and TechnologySchool of Information Science and TechnologySchool of Automation and Electronic EngineeringSchool of Automation and Electronic EngineeringIn the process of charging and using electric vehicles, lithium battery may cause hazards such as fire or even explosion due to thermal runaway. Therefore, a target detection model based on the improved YOLOv5 (You Only Look Once) algorithm is proposed for the features generated by lithium battery combustion, using the K-means algorithm to cluster and analyse the target locations within the dataset, while adjusting the residual structure and the number of convolutional kernels in the network and embedding a convolutional block attention module (CBAM) to improve the detection accuracy without affecting the detection speed. The experimental results show that the improved algorithm has an overall mAP evaluation index of 94.09%, an average F1 value of 90.00%, and a real-time detection FPS (frames per second) of 42.09, which can meet certain real-time monitoring requirements and can be deployed in various electric vehicle charging stations and production platforms for safety detection and will provide a guarantee for the safe production and development of electric vehicles in the future.http://dx.doi.org/10.1155/2023/9215528 |
| spellingShingle | Yuchen Gao Qing Yang Shiyu Zhang Dexin Gao Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring Journal of Electrical and Computer Engineering |
| title | Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring |
| title_full | Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring |
| title_fullStr | Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring |
| title_full_unstemmed | Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring |
| title_short | Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring |
| title_sort | real time fire detection method based on computer vision for electric vehicle charging safety monitoring |
| url | http://dx.doi.org/10.1155/2023/9215528 |
| work_keys_str_mv | AT yuchengao realtimefiredetectionmethodbasedoncomputervisionforelectricvehiclechargingsafetymonitoring AT qingyang realtimefiredetectionmethodbasedoncomputervisionforelectricvehiclechargingsafetymonitoring AT shiyuzhang realtimefiredetectionmethodbasedoncomputervisionforelectricvehiclechargingsafetymonitoring AT dexingao realtimefiredetectionmethodbasedoncomputervisionforelectricvehiclechargingsafetymonitoring |