Intelligent Recognition of Fire Video Image in Gas Station Based on YOLOv5
In view of the possible problems such as slow response of early open flame identification in the current on-site monitoring and early warning process of gas stations,more than 100 000 cases of fire image dataset were constructed through on-site simulation experiments and network acquisition,the YOLOv5s neural network structure was improved,and an early flame target detection model suitable for petrochemi-cal gas stations and other scenes was developed.The experimental results showed that the improved model had improved in recognition accuracy,recall rate and average recognition accuracy,etc.Random sampling of fire accident images of gas stations for effect testing can achieve 100%recognition accuracy and 96%re-call rate.On this basis,the intelligent monitoring platform of early fire of gas station was constructed to provide effective early warning support for emergency fire response under sudden fire.
YOLOv5object detectionearly firedeep learningintelligent identificationgas stationfire video
国家科技期刊平台
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