基于残差网络的配电柜设备元件状态识别

doi:10.3969/j.issn.1000-5641.2023.02.014

摘要/Abstract

摘要：

随着工业智能巡检的不断发展, 基于数字图像处理方法的设备元件状态识别系统被广泛应用. 为提升配电室中配电柜设备元件状态识别的准确率, 提出了一种基于残差网络(residual networks, ResNet)的设备元件状态识别方法. 首先搭建数据采集系统, 构建数据集; 然后对配电柜图像, 裁剪预设的设备元件目标区域, 生成设备元件图像; 对于设备元件图像, 构建基于ResNet的元件状态识别模型并训练; 使用训练完毕的模型识别元件的状态. 以变电站配电室中配电柜设备元件数据集作为研究对象, 对于特征复杂的元件采用单预测头的网络, 对于特征简单的元件采用多预测头的网络; 然后使用紧凑和剪枝的模型压缩方法在精度损失较小的情况下减少参数量和计算量; 最后介绍巡检系统的架构设计, 将JetSon Nano边缘终端作为算法模块的运行硬件, 以减少通信成本.

关键词: 智能巡检, 残差网络, 图像识别, 模型压缩

Abstract:

With the continuous development of industrial intelligent inspection technology, the equipment element state recognition system based on digital image processing is widely used. In order to improve the accuracy of power distribution cabinet(PDC) equipment element state recognition in a distribution room, a ResNet(residual networks)-based equipment element state recognition method is proposed. Firstly, the data acquisition system is set up and the data set is constructed. Then, for the PDC image, the preset device component target area is cropped to generate the device component image. For device component images, a ResNet-based component state recognition model was constructed and trained, and the trained model was used to identify component states. Taking the data set for power distribution cabinet equipment element in substation distribution rooms as the research object, a network of single prediction heads is adopted as the component with complex features, and the network of multiple prediction heads is adopted as the component with simple features. Then, the compact and pruning model compression method is used to reduce the number of parameters and the calculation amount under the condition of less accuracy loss. Finally, the architecture design of the inspection system is introduced. A JetSon Nano edge terminal is used as the running hardware of the algorithm module to reduce the communication cost.

Key words: intelligent patrol inspection, residual networks (ResNet), image recognition, model compression

中图分类号:

TP391

张洋, 赖叶静, 黄定江. 基于残差网络的配电柜设备元件状态识别[J]. 华东师范大学学报（自然科学版）, 2023, 2023(2): 132-142.

Yang ZHANG, Yejing LAI, Dingjiang HUANG. Device component state recognition method of power distribution cabinet based on a residual networks[J]. Journal of East China Normal University(Natural Science), 2023, 2023(2): 132-142.

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模型	训练集准确率/%	验证集准确率/%	params(M)	FLOPS(G)
7 预测头模型	99.99	99.83	11.184720	0.230243904
指示器单预测头模型	99.97	99.97	11.178564	0.230237760
指示灯单预测头模型	100.00	99.98	11.177538	0.230236736

模型	训练集准确率/%	验证集准确率/%	params(M)	FLOPS(G)
储能状态模型	99.99	99.93	11.177538	0.230236736
压板模型	99.99	99.99	11.177538	0.230236736
开关模型	99.99	99.94	11.177538	0.230236736

模型	训练集准确率/%	验证集准确率/%	params(M)	FLOPS(G)
按钮模型	99.99	99.92	11.177538	0.230236736
旋钮1模型	99.99	99.88	11.178051	0.230237248
旋钮2模型	99.99	99.89	11.178051	0.230237248
箭头模型	99.99	99.93	11.177538	0.230236736

模型	训练集准确率/%	验证集准确率/%	params(M)	FLOPS(G)
7 预测头模型	99.99	98.02	7.010448	0.146089984
指示器单预测头模型	100.00	99.98	7.004292	0.146083840
指示灯单预测头模型	100.00	99.99	7.003266	0.146082816

模型	训练集准确率/%	验证集准确率/%	params(M)	FLOPS(G)
7 预测头模型	99.99	91.50	9.708624	0.176797760
指示器单预测头模型	100.00	99.98	9.702468	0.176791616
指示灯单预测头模型	100.00	99.99	9.701442	0.176790592