摘要
面向电力巡检的目标检测是指对无人机采集到的图像进行分析,检测电力线路中的部分缺陷,从而对线路及时检修,保证电力系统能正常工作。基于深度学习的目标检测算法能高效处理大量的图片数据,其处理结果能应用于电力目标的故障诊断等任务,且众多算法的检测精度和速度都优于传统人工设计的机器学习方法。本文对基于深度学习的目标检测算法在电力巡检上的应用进行了较为全面的综述,并对比分析各种算法的优缺点,总结电力巡检领域的发展现状,还讨论了目标检测算法的未来发展趋势以及应用在电力巡检领域所面临的挑战。
Object detection for power inspection refers to the analysis of the images collected by UAV and the detection of defects in power lines,which enables the timely repairing of power lines and the normal operation of power system.The object detection algorithm based on deep learning can efficiently process a large amount of image data,and its processing results can be applied to tasks such as fault diagnosis of power targets.Moreover,many algorithms based on deep learning have better detection accuracy and higher speed than traditional manually designed machine learning methods.This paper provides a comprehensive review of the application of deep learning based object detection algorithms in power inspection.The advantages and disadvantages of various algorithms are compared and analyzed,the current development status in the field of power inspection are summarized,and the future development trends of object detection algorithms and the challenges faced in the field of power inspection are discussed.
作者
柯澳
王宇聪
KE Ao;WANG Yucong(Key Laboratory of Intelligent Control and Maintenance of Power Equipment,School of Electrical Engineering,Guangxi University,Guangxi Nanning 530004,China)
出处
《广西电力》
2022年第6期47-56,73,共11页
Guangxi Electric Power
关键词
电力巡检
目标检测
缺陷识别
神经网络
深度学习
power inspection
object detection
defect identification
neural networks
deep learning
