一种自动读取指针式仪表读数的方法

doi:10.6040/j.issn.1672-3961.0.2018.275

摘要/Abstract

摘要：

为解决变电站中自动化监控仪表读数的问题，提出基于机器学习和图像处理算法的指针式仪表自动读数方法，由仪表检测和指针识别两个阶段组成。使用全卷积网络(fully convolutional networks，FCN)对输入图像进行语义分割，以检测仪表的位置并提取仪表部分的图像。利用直方图均衡化、中值滤波和双边滤波减小光照和阴影对指针识别的干扰，并利用仿射变换矫正拍摄时的倾斜，再结合改进的霍夫变换识别仪表中指针的位置，从而计算指针角度获取读数。结果表明，对于自然场景中变电站中的指针式仪表，本研究能很好地检测出仪表并识别出指针的读数，对于光照和阴影等干扰具有良好的鲁棒性，可以显著减少变电站巡检人员的工作量，提高工作效率。

关键词: 指针仪表, 语义分割, 全卷积网络, 仿射变换, 霍夫变换

Abstract:

An automatic reading method for automatically monitoring pointer meter in substation was proposed based on the machine learning and image processing algorithms, which was consisted of two stages: meter detection and pointer recognition. The position of the meter in the input image was detected by using the fully convolutional networks, and then the patch of the meter was extracted. The interference of illumination and shadow on the pointer recognition was reduced by using histogram equalization, median filtering and bilateral filtering, and the tilt of shooting was rectified by using the affine transformation. The position of the pointer was detected via the improved Hough transform. The reading was obtained by computing the angle of the pointer. The results showed that the method could detect the pointer meter and recognize the reading accurately for the pointer instrument in the substation. The method showed good robustness to the disturbances such as illumination and shadow, which could significantly reduce the substation inspection personnel workload and improve the work efficiency.

Key words: pointer meter, semantic segmentation, FCN, affine transformation, Hough transform

中图分类号:

TP319.4

周杨浩,刘一帆,李瑮. 一种自动读取指针式仪表读数的方法[J]. 山东大学学报 (工学版), 2019, 49(4): 1-7.

Yanghao ZHOU,Yifan LIU,Li LI. An automatic reading method for pointer meter[J]. Journal of Shandong University(Engineering Science), 2019, 49(4): 1-7.

图/表 6

图1

图2

图3

表1

图4

图5

参考文献 20

1	陶冰洁, 韩佳乐, 李恩. 一种实用的指针式仪表读数识别方法[J]. 光电工程, 2011, 38 (4): 145- 150. doi: 10.3969/j.issn.1003-501X.2011.04.025
	TAO Bingjie , HAN Jiale , LI En . A practical pointer meter reading recognition method[J]. Opto-Electronic Engineering, 2011, 38 (4): 145- 150. doi: 10.3969/j.issn.1003-501X.2011.04.025
2	蒋薇.基于图像识别的指针式仪表数据处理终端研究[D].青岛:青岛大学, 2014.
	JIANG Wei. Research on pointer instrument data processing terminal based on image recognition[D]. Qingdao: Qingdao University, 2014.
3	赵菁.基于图像处理的指针式仪表识别设计[D].西安:西安电子科技大学, 2011.
	ZHAO Jing. Pointer type instrument recognition design based on image processing[D]. Xi′an: Xidian University, 2011.
4	孙琳, 王永东. 指针式仪表自动检定系统图像识别技术[J]. 现代电子技术, 2011, 34 (8): 101- 104. doi: 10.3969/j.issn.1004-373X.2011.08.032
	SUN Lin , WANG Yongdong . Pointer meter automatic verification system image recognition technology[J]. Modern Electronics Technique, 2011, 34 (8): 101- 104. doi: 10.3969/j.issn.1004-373X.2011.08.032
5	徐洋. 基于图像处理的汽车指针仪表检测研究[J]. 计算机应用与软件, 2014, 31 (8): 219- 221. doi: 10.3969/j.issn.1000-386x.2014.08.054
	XU Yang . Research on vehicle pointer instrument detection based on image processing[J]. Computer Applications and Software, 2014, 31 (8): 219- 221. doi: 10.3969/j.issn.1000-386x.2014.08.054
6	何智杰, 张彬. 高精度指针仪表自动读数识别方法[J]. 计算机辅助工程, 2006, 15 (3): 9- 12. doi: 10.3969/j.issn.1006-0871.2006.03.003
	HE Zhijie , ZHANG Bin . High-precision pointer meter automatic reading recognition method[J]. Computer Aided Engineering, 2006, 15 (3): 9- 12. doi: 10.3969/j.issn.1006-0871.2006.03.003
7	王瑞, 李琦. 一种基于改进角度法的指针式仪表图像自动读数方法[J]. 电测与仪表, 2013, 50 (11): 115- 118. doi: 10.3969/j.issn.1001-1390.2013.11.026
	WANG Rui , LI Qi . Pointer type instrument image automatic reading method based on improved angle method[J]. Electrical Measurement & Instrumentation, 2013, 50 (11): 115- 118. doi: 10.3969/j.issn.1001-1390.2013.11.026
8	朱海霞. 基于改进Hough变换和BP网络的指针仪表识别[J]. 电测与仪表, 2015, 52 (5): 11- 14. doi: 10.3969/j.issn.1001-1390.2015.05.003
	ZHU Haixia . Pointer meter recognition based on improved Hough transform and BP network[J]. Electrical Measurement & Instrumentation, 2015, 52 (5): 11- 14. doi: 10.3969/j.issn.1001-1390.2015.05.003
9	RE NS , HE K , GIRSHICK R . Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39 (6): 1440- 1448.
10	LIU WEI, ANGUELOV DRAGOMIR, ERHAN DUMITRU. SSD: single shot multibox detector[C]//European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 21-37.
11	REDMON JOSEPH, DIVVALA SANTOSH, GIRSHICK ROSS, et al. You only look once: unified, real-time object detection[C]// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, USA: IEEE, 2016: 779-788.
12	HE KAIMING, GKIOXARI GEORGIA, DOLLÁR PIOTR, et al. Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision (ICCV). Seattle, USA: IEEE, 2017: 2980-2988.
13	RONNEBERGER Olaf, FISCHER Philipp, BROX Thomas. U-Net: convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2015: 234-241.
14	CHEN Liang , PAPANDREOU George , KOKKINOS Iasonas , et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40 (4): 834- 848. doi: 10.1109/TPAMI.2017.2699184
15	LIN Guosheng, MILAN Anton. RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern. Seattle, USA: IEEE, 2017: 5168-5177.
16	PENG Chao, ZHANG Xiangyu, YU Gang, et al. Large kernel matters: improve semantic segmentation by global convolutional network[C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, USA: IEEE, 2017: 4353-4361.
17	LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//IEEE Conference on Computer Vision & Pattern Recognition. Seattle, WA, USA: IEEE, 2015: 3431-3440.
18	ZEILERM D, FERGUS R. Visualizing and understanding convolutional networks[C]// European Conference on Computer Vision. Berlin, Germany: Springer, 2014: 818-833.
19	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Representations (ICLR). San Diego, USA: ICLR, 2015.
20	KINGMA D P, BA J. Adam: a method for stochastic optimization[C]//International Conference on Learning Representations (ICLR). San Diego, USA: ICLR, 2015.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

网络	训练集平均精度/%	验证集平均精度/%
Ⅰ	91.558 7	91.478 9
Ⅱ	91.704 6	91.738 9