基于深度学习的无人机电网巡检缺陷检测研究

UAV power grid inspection defect detection based on deep learning

由于无人机电网巡检存在检测区域面积小、背景复杂、计算量大等特点,导致深度学习算法的准确率和实时性难以实现。为实现无人机电网巡检的准确、快速识别,分析了各类深度学习算法在复杂环境下对绝缘子的检测效果,提出了一种基于YOLO v3的目标检测算法。首先选用ResNet18作为主干网络结构,然后构建一个多尺度特征金字塔,将其与主干网络进行融合,形成深度融合的电网巡检绝缘子检测模型,可在提高检测准确率的同时,满足实时性的检测要求。实验结果表明,YOLO v3网络的均值平均精度(mAP)达98.10%,相比于Faster R-CNN提高了6.71%;其每秒检测帧数高达47.52帧,分别是R-CNN和Faster R-CNN的25倍和12倍。所提的YOLO v3网络具有更优的识别精度和检测速度。

In power grid inspection by an unmanned aerial vehicle(UAV),traditional deep learning algorithms may fail to achieve the high-accuracy and operate in real time owing to the characteristics of small detection area,complex background and intensive computation.To achieve accurate and fast identification of UAV power grid inspection,the detection effects of various deep learning algorithms are analyzed in detail,and an improved target detection algorithm based on YOLO v3 is proposed.The ResNet18 is adopted as the backbone network structure and a multi-scale feature pyramid is constructed.Then a deeply integrated grid inspection model is built to detect insulators via aligning the ResNet18 with the multi-scale feature pyramid,by which the detection can be executed in real time with a high-accuracy.Specifically,the mean average precision of the YOLO v3 network is 98.10%,which is increased by 6.71%over that of Faster R-CNN.Also,YOLO v3 detects up to 47.52 frames per second,25 times and 12 times R-CNN and Faster R-CNN,respectively.The improved YOLO v3 network has better identification accuracy and detection speed.