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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.<\/jats:p>","DOI":"10.1155\/2023\/9215528","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T18:50:17Z","timestamp":1692298217000},"page":"1-12","source":"Crossref","is-referenced-by-count":5,"title":["Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9282-6464","authenticated-orcid":true,"given":"Yuchen","family":"Gao","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8355-5826","authenticated-orcid":true,"given":"Qing","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong 266061, China"}]},{"given":"Shiyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8697-695X","authenticated-orcid":true,"given":"Dexin","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266061, China"}]}],"member":"311","reference":[{"issue":"14","key":"1","doi-asserted-by":"crossref","first-page":"252","DOI":"10.3901\/JME.2022.14.252","article-title":"Overview of \u201cMechanical abuse-thermal runaway\u201d of electric vehicle power battery and its safety prevention and control technology","volume":"58","author":"T. 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