摘要
火灾发生初期是灭火的最佳时期,故对于初期火灾的探测具有十分重要的意义。初期火灾的火焰面积较小,数据样本较少,传统的机器学习目标检测方法难以对其进行有效的训练。针对以上问题,提出图像型初期火灾探测系统,并对基于多重迁移学习训练得到的Yolo V5初期火灾探测模型进行重点研究。试验结果表明,该模型精确率达到97%,对初期火灾的探测精度高、探测速度快,可以快速准确地探测到初期火灾的发生。
The initial stage of fire is the best time to extinguish the fire,so it has a very important significance for the initial fire detection.The flame area of the initial fire is small and the data samples are few,the traditional machine learning target detection method is difficult to train effectively.In view of the above problems,an image early fire detection system is proposed,and the model based training is studied.The test results show that the model has an accuracy of 97%,the initial fire detection accuracy is high,and the detection speed is fast,and the initial fire can be detected quickly and accurately.
作者
蒋文萍
蒋珍存
JIANG Wen-ping;JIANG Zhen-cun(Institute of Electrical and Electronic Engineering,Shanghai University of Applied Sciences,Shanghai 201418,China)
出处
《消防科学与技术》
CAS
北大核心
2021年第1期109-112,共4页
Fire Science and Technology
基金
国家自然科学基金资助项目(61703279)。
