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 (4): 557-561.

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基于改进YOLOv3 的无人机林火监测系统设计与实现

刘青，刘志国，刘守全，孙淼，马宪伟   

  1. 国网北京电力公司电缆分公司，北京100010
  • 出版日期:2021-04-15 发布日期:2021-04-15
  • 作者简介:刘青(1984-)，男，国网北京市电力公司电缆分公司高级工程师，硕士，主要从事高压电缆运维检修工作，北京市朝阳区建外街道月河胡同2 号，100010。

Design and implementation of UAV forest fire monitoring system based on improved YOLOv3

LIU Qing, LIU Zhi-guo, LIU Shou-quan, SUN Miao, MA Xian-wei   

  1. Cable Branch of State Grid Beijing Electric Power Company,Beijing 100010, China
  • Online:2021-04-15 Published:2021-04-15

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摘要： 以无人机为飞行平台，基于目标检测技术设计并实现了一套林火监测系统，针对原生态YOLOv3 算法在烟雾与火焰检测时表现不佳，从增加NMS 和重新聚类Anchors 两个方面对算法进行改进，检测准确率和召回率分别提高了6.8% 、11.92%。该系统无论昼夜都可实时监测森林火灾，提高了森林防火安全防范的能力和林火预警的自动化、数字化水平。

关键词: 森林火灾, 无人机, 目标识别, YOLOv3

Abstract: Forest fire has the characteristics of uncontrollability and random spreading, so forest fire prevention is very important. Due to the vast forest area, the traditional forest fire monitoring method has many disadvantages, such as blind area, poor real- time performance, large resource consumption and so on. In this paper, a forest fire monitoring system is designed and implemented based on the target detection technology with UAV as the flight platform. Aiming at the poor performance of the original ecological YOLOv3 algorithm in smoke and flame detection, the algorithm is improved by adding NMS and re clustering anchors. The detection accuracy and recall rate are increased by 6.8% and 11.92% respectively. The system can monitor forest fire in real time, and improve the ability of forest fire prevention and the automation and digitization level of forest fire warning.

Key words:  forest fire, UAV, target recognition, YOLOv3