基于深度学习的绝缘子故障检测研究

Research on insulator fault detection based on deep learning

绝缘子是架空线路中重要组成部分之一,当出现故障时,影响电网安全运行。为实现绝缘子故障快速、精准的识别,提出了一种基于改进YOLOv3-Tiny的绝缘子故障检测方法。首先,为了增强小目标检测能力,对浅层特征图与第二检测层之前特征图进行同维拼接构建第三预测层。随后,该网络采用Ghost模块替换主干网络中的卷积层,降低模型的参数量。然后,设计了一个新的注意力模块MECA,不仅能够多尺度信息融合,还能使网络专注绝缘子的显著特征。最后,提出了新的交并比EIoU作为边框回归损失函数,更好的定位目标位置。实验结果表明,改进的YOLOv3-Tiny在绝缘子故障检测中平均准确率(MAP)高达96.1%,较原始YOLOv3-Tiny算法MAP提高了17%。

Insulator is one of the important components of overhead lines.When there is a fault,it will affect the safe operation of power grid.In order to realize rapid and accurate identification of insulator fault,an insulator fault detection method based on improved YOLOv3-Tiny is proposed.Firstly,in order to enhance the small target detection ability,the shallow feature map and the feature map before the second detection layer are spliced in the same dimension to construct the third prediction layer.Then,the network uses Ghost module to replace the convolution layer in the backbone network and reduce the parameters of the model.Then,a new attention module MECA(multiscale efficient channel attention)is designed to enable the network to focus on the salient characteristics of insulators.Finally,a new effective intersection over union(EIoU)is proposed as the frame regression loss function to better locate the insulator position.The experimental results show that the average accuracy(MAP)of the improved YOLOv3-Tiny algorithm in insulator fault detection is as high as 96.1%,which is 17%higher than that of the original YOLOv3-Tiny.