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文章摘要
基于四旋翼无人机的二次屏柜巡检系统
Secondary screen inspection system based on quadrotor UAV
Received:December 19, 2019  Revised:December 19, 2019
DOI:10.19753/j.issn1001-1390.2022.07.024
中文关键词: 四旋翼无人机  AprilTag  树莓派  图像处理  CNN
英文关键词: quadrotor UAV, AprilTag, Raspberry Pi, image processing, CNN
基金项目:
Author NameAffiliationE-mail
Luo Xianyue TongRen Power Supply Bureau of Guizhou Power Grid CoLtd,Guizhou Tongren 149886442@qq.com 
Zhou Jingyu TongRen Power Supply Bureau of Guizhou Power Grid CoLtd,Guizhou Tongren 26870267@qq.com 
Yu Tianrun TongRen Power Supply Bureau of Guizhou Power Grid CoLtd,Guizhou Tongren 870517529@qq.com 
Pan Jun TongRen Power Supply Bureau of Guizhou Power Grid CoLtd,Guizhou Tongren 252410250@qq.com 
Zhou Siyuan TongRen Power Supply Bureau of Guizhou Power Grid CoLtd,Guizhou Tongren 371283340@qq.com 
Qi Donglian* College of Electrical Engineering, Zhejiang University liqi13520508908@163.com 
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中文摘要:
      随着人们对于电力的需求量越来越大,相应的电力巡检的要求也越来越高。文中设计了基于四旋翼无人机的二次屏柜巡检系统。首先为解决无人机在室内GPS拒止问题,文中通过AprilTag算法配合传感器高度信息,实现无人机室内飞行路径规划功能、姿态矫正、自主飞行。然后,在机载树莓派内存入任务程序,该程序将利用飞控和传感器实现对任务地点二维码的寻找和判断,并在达到指定点后执行拍摄任务。然后通过局域网将采集的图像传输到地面服务器,并输入到预先训练好的CNN网络模型中进行异常检测。依据识别结果判断二次屏柜是否工作正常,并将检测结果上传到显示平台。最终项目在贵州铜仁变电站室内二次屏柜现场测试成功。
英文摘要:
      As people''s demand for electricity grows more and more, the requirements for corresponding power inspections become higher and higher. A secondary screen cabinet inspection system based on a quadrotor drone is designed. First, in order to solve the indoor GPS rejection problem of the drone, the AprilTag algorithm is used in conjunction with the sensor height information to realize the indoor flight path planning function, attitude correction, and autonomous flight of the drone. Then, the mission program is stored in the onboard Raspberry Pi. This program will use the flight controller and sensors to find and judge the QR code of the mission location, and execute the shooting mission after reaching the designated point. Then, the collected images are transmitted to the ground server through the local area network, and input to the pre-trained CNN network model for anomaly detection. According to the recognition result, it is judged whether the secondary screen cabinet works normally, and the detection result is uploaded to the display platform. The final project was successfully tested in the indoor secondary screen cabinet of Tongren Substation in Guizhou.
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