主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2021, Vol. 40 ›› Issue (1): 109-112.

• • 上一篇    下一篇

基于多重迁移学习的Yolo V5 初期火灾探测研究

蒋文萍，蒋珍存   

  1. 上海应用技术大学电气与电子工程学院，上海201418
  • 出版日期:2021-01-15 发布日期:2021-01-15
  • 通讯作者: 蒋珍存(1997-)，男，上海应用技术大学电气与电子工程学院在读硕士研究生。
  • 作者简介:蒋文萍（1977-），女，上海人，上海应用技术大学电气与电子工程学院讲师，博士，主要从事智能控制方面的研究，上海市奉贤区海泉路100 号，201418。
  • 基金资助:
    国家自然科学基金资助项目(61703279)

Research on early fire detection of Yolo V5 based on multiple transfer learning

JIANG Wen-ping, JIANG Zhen-cun   

  1. Institute of Electrical and Electronic Engineering, Shanghai University of Applied Sciences, Shanghai 201418, China
  • Online:2021-01-15 Published:2021-01-15

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。

关键词: 初期火灾, 火灾探测, 目标检测, Yolo V5, 迁移学习, 计算机视觉

Abstract: 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.

Key words: initial fire detection, target detection, Yolo V5, transfer learning, computer vision