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 (3): 390-393.

• • 上一篇    下一篇

有雾天气下输电线路山火烟雾检测方法研究

刘志翔1，牛彪1，王帅1，陈青松1，龙雅芸2，江柳2   

  1. 1. 国网山西省电力公司电力科学研究院，山西太原300001；2. 华北电力大学（保定），河北保定071003
  • 出版日期:2021-03-15 发布日期:2021-03-15
  • 作者简介:刘志翔（1991-），男，山西大同人，国网山西省电力公司电力科学研究院工程师，硕士，主要从事电网设备技术监督工作，山西省太原市青年路6 号，300001。
  • 基金资助:
    国网山西省电力公司科技项目“基于影像识别的重要输电通道全天候预警监测分析技术研究与应用”（52053018000L）

Study on detection method of mountain fire smoke in transmission lines under fog weather

LIU Zhi-xiang1, NIU Biao1,WANG Shuai1, CHEN Qing-song1, LONG Ya-yun2, JIANG Liu2   

  1. 1. State Grid Shanxi Electric Power Research Institute, Shanxi Taiyuan 300001, China; 2. North China Electric Power University(Baoding), Hebei Baoding 071003, China
  • Online:2021-03-15 Published:2021-03-15

摘要： 输电线路多处于环境复杂的山林中，早期山火发生时经常以烟雾的形式呈现，而在有雾状况下的山火烟雾检测方法的研究却很少见。针对有雾天气状况时的山火检测，提出一种去雾图像增强方法，首先对图像局部均衡化处理，再对全局利用改进的单尺度Retinex 方法做增强处理，并使用基于卷积神经网络的山火烟雾检测网络来检测早期山火发生时产生的烟雾。实验结果表明，基于局部和全局的图像增强方法可使山火烟雾检测准确率有明显提升，通过卷积神经网络的烟雾检测准确率达到97.2%。

关键词: 输电线路, 图像去雾, 烟雾检测, 卷积神经网络

Abstract:

Most of the transmission lines are in the mountain forests with complex environment, and early mountain fires often appear in the form of smoke. But the study of smoke detection methods for mountain fires in foggy conditions is rare. Aiming at the detection of wildfires in foggy weather conditions, this paper proposes a de- fogging image enhancement method. First, perform the localized equalization of the image; then conduct enhanced process by the global single- scale Retinex method, and detect smoke generated during early wildfires using CNNbased wildfires smoke detection network. Experimental analysis proves that the local and global image enhancement methods can significantly improve the accuracy of wildfire smoke detection, and the accuracy of smoke detection through convolutional neural network reaches 97.2%. 

Key words: transmission line, image de- foggy, smoke detection, convolutional neural network