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 ›› 2025, Vol. 44 ›› Issue (4): 548-552.

• • 上一篇    

四旋翼无人机搭载计算机视觉双目定位林火技术

王慧颖1, 张志强1, 王兆强2   

  1. (1.中国消防救援学院,北京 102202; 2.火箭军大学,陕西 西安 710025)
  • 收稿日期:2024-01-09 修回日期:2024-06-25 出版日期:2025-04-15 发布日期:2025-04-15
  • 作者简介:王慧颖,中国消防救援学院副教授,博士,主要从事消防安全教育,北京市昌平区南口镇南雁路4号,102202,wanghuiying120454@163.com。
  • 基金资助:
    国家重点研发计划项目(2021YFC3001902、2023YFC3006904);国家自然科学基金项目(61873273)

Research on binocular positioning forest fire technology of quadrotor UAV based on computer vision

Wang Huiying1, Zhang Zhiqiang1, Wang Zhaoqiang2   

  1. (1. China Fire and Rescue Academy, Beijing 102202, China;2. Rocket Force Engineering University, Xi'an Shaanxi 710025, China)
  • Received:2024-01-09 Revised:2024-06-25 Online:2025-04-15 Published:2025-04-15

摘要: 为了提高林火防控策略的有效性,解决林火监测、火点快速识别和有效定位的难题,提出了基于四旋翼无人机搭载计算机视觉系统双目定位火点技术。首先,搭载红外热像仪、摄像头、激光雷达等设备检测出林区烟雾和隐藏火点,利用图像智能识别技术进行数据处理,实现火点精准识别;其次,采用双目平面影像算法,结合无人机自身姿态位置信息提出了火点定位数学模型,得到了火点位置坐标;最后,通过设计8组试验验证了该无人机识别定位系统的精确度和可靠性,结果表明经度最大误差为(6.20×10-6 )o,纬度最大误差为(3.21×10-6)o,高程最大误差为2.73×10-3 m,能够实现精准定位。研究成果为森林火灾防控提供了新的方法和思路。

关键词: 多传感器方法, 计算机视觉, 四旋翼无人机, 林火监测, 定位

Abstract: In In order to improve the effectiveness of forest fire prevention and control strategy and solve the problems of forest fire monitoring, rapid fire point identification and effective location, binocular fire point positioning technology of quadrotor UAV equipped with computer vision system was proposed. Firstly, it is equipped with infrared thermal imager, camera, laser radar and other equipment to detect forest smoke and hidden fire points, and use image intelligent recognition technology for data processing to achieve accurate fire point identification; Secondly, the binocular plane image algorithm combined with the UAV's own attitude and position information is used to propose a mathematical model of fire location, and the fire location coordinates are obtained. Finally, 8 sets of experiments were designed to verify the accuracy and reliability of the UAV identification and positioning system. The results show that the maximum error of longitude is (6.20×10-6 )o, the maximum error of latitude is ( 3.21×10-6)o, and the maximum error of elevation is 2.73×10-3 m, which can achieve accurate positioning. Therefore, it provides new methods and ideas for forest fire prevention and control.

Key words: multi-sensor method, computer vision, quadrotor unmanned aerial vehicle, forest fire monitoring, location