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文章摘要
基于无人机图像的输电线路部件检测方法研究
Research on detection method of transmission line components based on UAV image
Received:July 19, 2021  Revised:August 05, 2021
DOI:10.19753/j.issn1001-1390.2024.05.027
中文关键词: 无人机  输电线路  单级多框预测检测器  特征金字塔网络  目标检测
英文关键词: unmanned aerial vehicle, transmission line, single-stage multi-frame predictive detector, feature pyramid network, target detection
基金项目:基金项目:南方电网公司信息化重点项目(031900HK42200008)
Author NameAffiliationE-mail
Hanxian Han* Dongguan Power Supply Bureau of Guangdong Power Grid Corporation GuangDongDongGuan China hanxian1974@163.com 
Jinman Luo Dongguan Power Supply Bureau of Guangdong Power Grid Corporation GuangDongDongGuan China hanxian1974@163.com 
Liyuan Liu Dongguan Power Supply Bureau of Guangdong Power Grid Corporation GuangDongDongGuan China hanxian1974@163.com 
Shanlong Zhao Dongguan Power Supply Bureau of Guangdong Power Grid Corporation GuangDongDongGuan China hanxian1974@163.com 
Chengwen Xia China Southern Power Grid ShenZhen Digital Grid Research Institute Co,Ltd GuangDongShenZhen China hanxian1974@163.com 
Ailin Zhao College of Economics and Management,North China Electric Power UniversityBeiJing China hanxian1974@163.com 
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中文摘要:
      针对无人机电力巡检模式在图像快速检测方面存在的自动化程度和效率低等问题,提出了一种将单级多框预测检测器SSD与特征金字塔网络FPN相结合的输电线路部件检测方法,并对绝缘子故障进行检测。在SSD目标检测的基础上,加入了FPN特征金字塔结构,局部融合层间特征信息。实验验证了文中所提方法的优越性。实验结果表明,在部件检测中,该方法对大、中、小尺寸目标均具有良好的检测效果,检测精度在90%左右,在绝缘子故障检测中检测精度达到87.4%。为输电线路部件检测技术的发展提供了参考。
英文摘要:
      Aiming at the low automation and efficiency of unmanned aerial vehicle (UAV) power inspection mode in image fast detection, a transmission line component detection method combining single-stage multi-frame predictive detector (SSD) with feature pyramid network (FPN) is proposed, and the insulator fault is detected. On the basis of SSD target detection, the FPN feature pyramid structure is added to locally integrate the feature information between layers. The experimental results show the superiority of the proposed method. The experimental results show that the method has good detection effect for large, medium and small size targets in component detection, and the detection accuracy is about 90%, and the detection accuracy in insulator fault detection is 87.4%. It provides a reference for the development of transmission line component detection technology.
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