The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology pro...The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.展开更多
针对换流站多种电气设备检测时背景复杂干扰性强而又需要快速准确检测出故障的实际情况,提出基于改进YOLOv5(You Only Look Once)的检测方法。首先,为提高算法的准确性和收敛速度,通过K-means聚类算法对YOLOv5模型中的锚框预设进行改进...针对换流站多种电气设备检测时背景复杂干扰性强而又需要快速准确检测出故障的实际情况,提出基于改进YOLOv5(You Only Look Once)的检测方法。首先,为提高算法的准确性和收敛速度,通过K-means聚类算法对YOLOv5模型中的锚框预设进行改进,在数据集预处理阶段得到更适用于换流站电气设备的锚框,使其更加契合换流站电力设备数据集;然后,为提高算法检测过程的识别速度,在特征提取网络添加注意力机制模块,筛选出重要的特征信息。将改进后的算法网络识别效果与YOLOv5中的原始算法网络检测结果进行对比分析。结果表明,检测平均识别精度均值由71.16%提高至92.51%,检测速度由21帧/s提升至31帧/s;同时与R-CNN(Regions with convolutional neural networks)等算法相比,检测精度与速度都有较大提升。添加可解释性分析,将识别结果通过热力图的形式显示,可以更好地应对算法的潜在风险。展开更多
针对电力设备运行和维护中所产生的大量碎片化、非系统性以及相关性不足设备缺陷文本,提出了一种电力设备缺陷文本识别模型。使用基于全词掩码的预训练模型(bidirectional encoder representation from transform-ers,BERT)替换基于随...针对电力设备运行和维护中所产生的大量碎片化、非系统性以及相关性不足设备缺陷文本,提出了一种电力设备缺陷文本识别模型。使用基于全词掩码的预训练模型(bidirectional encoder representation from transform-ers,BERT)替换基于随机掩码的BERT模型,提高了对电力词汇的理解力。使用双向长短期记忆网络(bidirection-al long short-term memory,BiLSTM)提高了模型捕获上下文信息的能力,并提高了模型的鲁棒性,引入注意力机制(attention)可以更好地捕获电力设备缺陷实体之间的复杂依赖关系,从而进一步提升模型的表现。实验结果显示,该模型准确率、召回率、F1值分别为96.26%、96.94%、96.60%,在地点、缺陷内容和设备三种实体上的F1值均优于其他模型。展开更多
基金Jilin Science and Technology Development Plan Project(No.20200403075SF)Doctoral Research Start-Up Fund of Northeast Electric Power University(No.BSJXM-2018202).
文摘The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.
文摘针对换流站多种电气设备检测时背景复杂干扰性强而又需要快速准确检测出故障的实际情况,提出基于改进YOLOv5(You Only Look Once)的检测方法。首先,为提高算法的准确性和收敛速度,通过K-means聚类算法对YOLOv5模型中的锚框预设进行改进,在数据集预处理阶段得到更适用于换流站电气设备的锚框,使其更加契合换流站电力设备数据集;然后,为提高算法检测过程的识别速度,在特征提取网络添加注意力机制模块,筛选出重要的特征信息。将改进后的算法网络识别效果与YOLOv5中的原始算法网络检测结果进行对比分析。结果表明,检测平均识别精度均值由71.16%提高至92.51%,检测速度由21帧/s提升至31帧/s;同时与R-CNN(Regions with convolutional neural networks)等算法相比,检测精度与速度都有较大提升。添加可解释性分析,将识别结果通过热力图的形式显示,可以更好地应对算法的潜在风险。
文摘针对电力设备运行和维护中所产生的大量碎片化、非系统性以及相关性不足设备缺陷文本,提出了一种电力设备缺陷文本识别模型。使用基于全词掩码的预训练模型(bidirectional encoder representation from transform-ers,BERT)替换基于随机掩码的BERT模型,提高了对电力词汇的理解力。使用双向长短期记忆网络(bidirection-al long short-term memory,BiLSTM)提高了模型捕获上下文信息的能力,并提高了模型的鲁棒性,引入注意力机制(attention)可以更好地捕获电力设备缺陷实体之间的复杂依赖关系,从而进一步提升模型的表现。实验结果显示,该模型准确率、召回率、F1值分别为96.26%、96.94%、96.60%,在地点、缺陷内容和设备三种实体上的F1值均优于其他模型。
