基于深度学习和图匹配的接线图检测与校核

李昊; 王杉; 耿玉杰; 王黎; 孙文昌; 苗纯源

doi:10.13700/j.bh.1001-5965.2020.0478

基于深度学习和图匹配的接线图检测与校核

doi: 10.13700/j.bh.1001-5965.2020.0478

李昊^1, ,,
王杉¹,
耿玉杰²,
王黎¹,
孙文昌³,
苗纯源³

1.
国网山东省电力公司青岛供电公司电力调度控制中心, 青岛 266002
2.
国网山东省电力公司电力调度控制中心, 济南 250001
3.
山东大学(青岛) 计算机科学与技术学院, 青岛 266237

基金项目:

国网山东省电力公司科技项目 5206021900TW

详细信息

作者简介:
李昊  男，硕士，工程师。主要研究方向：电力调度自动化

王杉  男，技师。主要研究方向：电力调度自动化

耿玉杰  男，硕士，高级工程师。主要研究方向：电力调度自动化

王黎  女，硕士，高级工程师。主要研究方向：电力调度自动化与继电保护

孙文昌  男，硕士研究生。主要研究方向：计算机视觉

苗纯源  男，硕士研究生。主要研究方向：计算机视觉

通讯作者:
李昊, E-mail: lihao-0717@163.com

中图分类号: TP399;TM734
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- 文章访问数: 898
- HTML全文浏览量: 134
- PDF下载量: 194
- 被引次数: 0
出版历程
- 收稿日期: 2020-08-31
- 录用日期: 2020-09-11
- 网络出版日期: 2021-03-20

Wiring diagram detection and check based on deep learning and graph matching

1.
Electric Power Dispatching & Control Center, State Grid Qingdao Power Supply Company, Qingdao 266002, China
2.
Electric Power Dispatching & Control Center, State Grid Shandong Electric Power Company, Jinan 250001, China
3.
School of Computer Science and Technology, Shandong University(Qingdao), Qingdao 266237, China

Funds:

Science and Technology Project of State Grid Shandong Electric Power Company 5206021900TW

More Information

Corresponding author: LI Hao, E-mail: lihao-0717@163.com

摘要

摘要:
传统的厂站一次接线图的绘制和管理主要依靠电网运行人员，费时费力且缺乏科学可校核的参考标准。提出了一种基于深度神经网络和数字图像处理相结合的厂站一次接线图的自动检测、识别和校核算法。首先，使用目标检测Faster R-CNN模型检测厂站接线图中的电器元件，并达到92%的检测准确率，同时使用端到端的文字检测识别模型识别厂站接线图中的文字信息，并达到94.2%的文字检测准确率和92%的文字识别准确率；然后，使用数字图像处理技术进行厂站接线图连接线、拓扑关系识别；最后，使用改进的VF2算法进行厂站一次接线图和人工维护的厂站一次接线图拓扑关系匹配校核，将拓扑数据抽象为无向图，通过轮廓序号得到元件的相对位置信息，根据改进的VF2算法得到2张图的匹配率，并通过匹配率与设定好的阈值来帮助核验，相比于节点遍历的匹配方法，核验准确率提高了37.5%。基于某供电公司提供的部分变电站的厂站一次接线图标注了接线图电器元件，贡献了一个小型接线图数据集。
- 厂站一次接线图 /
- 拓扑关系匹配 /
- 自动生成 /
- 自动校核 /
- 深度学习 /
- 数字图像处理
Abstract:
The drawing and management of the traditional primary wiring diagram of plant and station mainly depend on the operators of power grid, which wastes time and labor and lacks scientific and verifiable reference standards. Towards this problem, based on the deep neural networks and digital image processing, we propose an algorithm for automatic detection, recognition and verification. Specifically, Faster R-CNN is first adopted to detect the electrical components of wiring diagram with 92% detection accuracy. In the meantime, an end-to-end text detection and recognition model is used to recognize the text with 94.2% detection accuracy and 92% character recognition accuracy. Then we take advantage of digital image processing technique to identify the connection of wiring diagram and topological relation. Finally, improved graph matching algorithm VF2 is used to check the difference between the electronic and manually maintained diagrams. The topological data is abstracted into an undirected graph, and the relative position information of components is obtained through the outline number. Based on the improved VF2 algorithm, we can compute the matching rate of two graphs to help the verification. Compared with the matching method of node traversal, the verification accuracy can be improved by 37.5%. Based on the first wiring diagram of some substations provided by a power supply company, this paper marks the electrical components of wiring diagram and contributes a small wiring diagram dataset.
- primary wiring diagram of plant and station /
- topological relation matching /
- automatic generation /
- automatic check /
- deep learning /
- digital image processing

HTML全文

图 1 厂站一次接线图识别流程

Figure 1. Identification flowchart of primary wiring diagram of plant and station

下载: 全尺寸图片幻灯片

图 2 厂站接线图多尺度融合检测算法

Figure 2. Multi-scale fusion detection algorithm for wiring diagram of plant and station

下载: 全尺寸图片幻灯片

图 3 厂站接线图文字识别流程

Figure 3. Text recognition process of wiring diagram of plant and station

下载: 全尺寸图片幻灯片

图 4 厂站接线图文字识别分割

Figure 4. Text recognition segmentation of wiring diagram of plant and station

下载: 全尺寸图片幻灯片

图 5 电子图示例

Figure 5. Example of electronic wiring diagram

下载: 全尺寸图片幻灯片

图 6 人工图示例

Figure 6. Example of manual wiring diagram

下载: 全尺寸图片幻灯片

图 7 电子图匹配结果

Figure 7. Matching results of electronic wiring diagram

下载: 全尺寸图片幻灯片

图 8 人工图匹配结果

Figure 8. Matching results of manual wiring diagram

下载: 全尺寸图片幻灯片

表 1 电器元件识别实验结果对比

Table 1. Comparison of electrical component recognition experiment results

类别	检测准确率/%
类别	YOLOv3	Faster R-CNN	Faster R-CNN融合算法
断路器	100	84	85.6
接地刀闸	92.7	90.5	96.6
接地	85.6	72.7	87.6
电抗	85.2	89.3	97.9
刀闸	95.3	86.7	94.8
变压器	96.9	100	100
避雷器	93.1	80	94.4
母线	87.5	85.9	97.2
手车开关	89.1	95.1	96
熔断器	93.9	92.7	93.3
电容	82.6	66.5	88.8
隔离手车1	87.2	0.72	76
隔离手车2	92.2	58.4	81.8
所变	94.6	86.1	88.9
电压互感器	96.4	88.6	85.7
电力电感器	75.4	83.3	94.4
忽略部件	80	66.7	80
平均	89.5	83.5	92

下载: 导出CSV

表 2 文字识别实验结果对比

Table 2. Comparison of text recognition experiment results

检测算法	文本区域检测准确率/%	文本识别准确率/%
Advanced EAST+Tesseract OCR	76.6	78
Attention-OCR	94.2	92

下载: 导出CSV

表 3 拓扑关系识别测试结果

Table 3. Test results of topological relation recognition

图片名称	正确的连接关系数	连接关系总数	准确率/%
10097	71	85	83.53
10099	115	119	96.64
10125	93	103	90.29
10129	67	80	83.75
20008	88	92	95.65
20009	68	104	65.38
20014	149	151	98.68

下载: 导出CSV

表 4 两种方法的匹配率结果

Table 4. Matching rate results of two methods

测试组别	匹配率/%
测试组别	节点遍历方法	改进的VF2算法
第1组	27.97	55.95
第2组	37.50	51.61
第3组	0.74	54.74
第4组	62.07	63.49
第5组	24.42	76.67
第6组	9.33	92.00
第7组	93.24	92.86
第8组	0	71.69

下载: 导出CSV

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