基于深度学习的配电网线路设备缺陷智能检测

Intelligent Defect Detection of Distribution Line Equipment Based on Deep Learning

无人机配电线路巡检已经广泛开展应用,线路设备缺陷人工识别过程复杂、工作量较大等问题仍然存在。为了提高作业效率,根据无人机发展现状,对现有数据进行深入挖掘,利用标记系统对数据进行处理,再利用深度学习算法实现配电网无人机自动巡检、缺陷自动研判。该算法是基于残差双尺度检测器的巡检目标智能检测,优势在于可识别两种规格尺寸的目标对象,相比于传统的双阶段目标检测方案,其运行速度更快,更适合在终端资源受限设备中运行。目前该算法已达到95%的准确率。

Unmanned aerial vehicle(UAV)inspection on power distribution line has been widely applied.However,some problems remain unresolved-complex process and heavy workload in manual identification of line equipment defects.To improve the operation efficiency,this paper carries out in-depth mining on the existing data and processes the data with the marking system;then it implements automatic inspection of the UAV on the distribution network and automatic defect judgment in distribution network with the help of deep learning.As an intelligent inspection target detection based on residual dual scale detector,the method is advantageous in recognizing target of two sizes with higher operation speed and is more suitable for operation in the terminal resource-constrained devices.At present,this accuracy of the algorithm reaches up to 95%.