巡检机器人中指针式仪表示数的自动识别方法

doi:10.11772/j.issn.1001-9081.2018061275

计算机应用 ›› 2019, Vol. 39 ›› Issue (1): 287-291.DOI: 10.11772/j.issn.1001-9081.2018061275

• 虚拟现实与多媒体计算 • 上一篇下一篇

巡检机器人中指针式仪表示数的自动识别方法

孙婷^1,2, 马磊^1,2

1. 西南交通大学电气工程学院, 成都 610031;
2. 西南交通大学系统科学与技术研究所, 成都 610031

收稿日期:2018-06-20 修回日期:2018-08-08 出版日期:2019-01-10 发布日期:2019-01-21
通讯作者: 马磊
作者简介:孙婷(1993-),女,安徽安庆人,硕士研究生,主要研究方向:图像处理、机器人控制;马磊(1972-),男,贵州贵阳人,教授,博士,主要研究方向:机器人控制、多机器人系统、新能源控制。

Automatic recognition method of pointer meter for inspection robots

SUN Ting^1,2, MA Lei^1,2

1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
2. Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China

Received:2018-06-20 Revised:2018-08-08 Online:2019-01-10 Published:2019-01-21

摘要/Abstract

摘要： 针对巡检机器人室外自主识别仪表示数易受到光照影响的问题，在研究了基于二维伽马函数的仪表图像光照不均匀自适应校正算法的基础上，提出了基于最大稳定极值区域（MSER）提取指针区域的算法。首先，通过三尺度高斯函数提取光照分量，构造二维伽马函数自动地调整图像反光区域或过暗区域的亮度；然后，通过MSER的两次稳定区域检测提取指针区域；接着，以指针通过仪表轴心为条件，用细化算法和累计概率霍夫变换（PPHT）精确地定位到指针，提高了定位直线的准确度；最后，通过PPHT检测的直线两个端点与轴心位置比较，直接可以判断指针指向，更加方便了计算示数。实验结果表明，所提的仪表示数识别方法能够适应不同光照下、不同类型仪表的指针定位，且识别示数的正确率达到94%以上。

关键词: 二维伽马函数, 指针区域提取, 最大稳定极值区域(MSER), 细化算法, 累计概率霍夫变换(PPHT)

Abstract: In the outdoor working environment of inspection robots, recognizing the number of meter was susceptible to illumination. An adaptive adjustment algorithm for meter images based on 2D-Gamma function was studied. Then an algorithm based on Maximally Stable Extremal Region (MSER) was proposed to extract the pointer. Firstly, the reflection component was extracted by three-scale Gaussian functions, 2D Gamma function was constructed to automatically adjust brightness of the reflected or overshadowed region of image. Secondly, the pointer region was extracted through two MSER detections. Thirdly, on the condition that the pointer passed through the axis of dial, the pointer was precisely positioned by thinning algorithm and Progressive Probabilistic Hough Transform (PPHT) to improve the accuracy of positioning lines. Finally, on basis of comparing the positions of two endpoints by PPHT with axis, the direction of pointer was directly determined, thus calculating the number was more convenient. The experimental results show that the proposed method can deal with different types of meters under different lighting conditions. Moreover, the correct rate of identification reaches over 94%.

Key words: 2D-Gamma function, extraction of pointer region, Maximally Stable Extremal Region (MSER), thinning algorithm, Progressive Probabilistic Hough Transform (PPHT)

中图分类号:

孙婷, 马磊. 巡检机器人中指针式仪表示数的自动识别方法[J]. 计算机应用, 2019, 39(1): 287-291.

SUN Ting, MA Lei. Automatic recognition method of pointer meter for inspection robots[J]. Journal of Computer Applications, 2019, 39(1): 287-291.

参考文献

[1] 鲁守银,张营,李建祥,等.移动机器人在高压变电站中的应用[J].高电压技术,2017,43(1):276-284.(LU S Y, ZHANG Y, LI J X, et al. Application of mobile robot in high voltage substation[J]. High Voltage Engineering, 2017, 43(1):276-284.)
[2] LIU S G, LIU M Y, HE Y. Checking on the quality of gauge panel based on wavelet analysis[C]//Proceedings of the 2002 International Conference on Machine Learning and Cybernetics. Washington, DC:IEEE Computer Society, 2002:763-767.
[3] JIAN Y, XIN W, XUE Z, et al. Cloud computing and visual attention based object detection for power substation surveillance robots[C]//Proceedings of the 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering. Piscataway, NJ:IEEE, 2015:337-342.
[4] 许丽,石伟,方甜.巡检机器人中的指针式仪表读数识别系统[J].仪器仪表学报,2017,38(7):1782-1790.(XU L, SHI W, FANG T. Pointer meter reading recognition system used in patrol robot[J]. Chinese Journal of Scientific Instrument, 2017, 38(7):1782-1790.)
[5] TAN R, IKEUCHI K. Separating reflection components of textured surfaces using a single image[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(2):178-193.
[6] LEE P, WU S, HUANG Y P. Illumination compensation using oriented local histogram equalization and its application to face recognition[J]. IEEE Transactions on Image Processing, 2012, 21(9):4280-4289.
[7] WANG S, ZHENG J, HU H M, et al. Naturalness preserved enhancement algorithm for non-uniform illumination images[J]. IEEE Transactions on Image Processing, 2013, 22(9):3538-3548.
[8] ELAD M. Retinex by two bilateral filters[C]//Proceedings of the 2005 International Conference on Scale-Space Theories in Computer Vision. Berlin:Springer, 2005:217-229.
[9] 刘志成,王殿伟,刘颖,等.基于二维伽马函数的光照不均匀图像自适应校正算法[J].北京理工大学学报,2016,36(2):191-196.(LIU Z C, WANG D W, LIU Y, et al. Adaptive adjustment algorithm for non-uniform illumination images based on 2D Gamma function[J].Transactions of Beijing Institute of Technology, 2016,36(2):191-196.)
[10] 牟少敏,杜海洋,苏平,等.一种改进的快速并行细化算法[J].微电子学与计算机,2013,30(1):53-55.(MOU S M, DU H Y, SU P, et al. A new improved fast parallel thinning algorithm[J]. Microelectronics and Computer, 2013, 30(1):53-55).
[11] 李益红,周晓谊.一种多分辨率多尺度的Retinex彩色图像增强算法[J].计算机工程与应用,2017,53(16):193-198.(LI Y H, ZHOU X Y. Multi-resolution and multi-scale Retinex for color image enhancement[J]. Computer Engineering and Applications, 2017, 53(16):193-198.)
[12] 彭国福,林正浩.图像处理中GAMMA校正的研究和实现[J].电子工程师,2006,32(2):30-32.(PENG G F, LIN Z H. A study on GAMMA correction and its implementation in image processing[J]. Electronic Engineer, 2006, 32(2):30-32.)
[13] MATAS J, CHUM O, URBAN M, et al. Robust wide baseline stereo from maximally stable regions[J]. Image and Vision Computing, 2004, 22(10):761-767.
[14] 唐有宝,卜巍,邬向前.多层次MSER自然场景文本检测[J].浙江大学学报(工学版),2016,50(6):1134-1140.(TANG Y B, BU W, WU X Q. Natural scene text detection based on multi-level MSER[J]. Journal of Zhejiang University (Engineering Science), 2016, 50(6):1134-1140.)
[15] NISTER D, STEWENIUS H. Linear time maximally stable extremal regions[C]//Proceedings of the 10th European Conference on Computer Vision. Berlin:Springer, 2008:183-196.
[16] BAKER L, MILLS S, LANGLOTZ T, et al. Power line detection using Hough transform and line tracing techniques[C]//Proceedings of the 2016 IEEE International Conference on Image and Vision Computing. Piscataway, NJ:IEEE, 2017:1-6.
[17] YE H S, SHANG G C, WANG L N, et al. A new method based on Hough transform for quick line and circle detection[C]//Proceedings of the 2016 IEEE International Conference on Biomedical Engineering and Informatics. Piscataway, NJ:IEEE, 2016:52-56.

巡检机器人中指针式仪表示数的自动识别方法

Automatic recognition method of pointer meter for inspection robots

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

[1]	李丽娜甘晓晔徐攀峰马俊. 改进粒子群优化Takagi-Sugeno模糊径向基函数神经网络的非线性系统建模[J]. 计算机应用, 2014, 34(5): 1341-1344.
[2]	杨森孟晨王成. 电子系统健康状态监测数据优化算法[J]. 计算机应用, 2012, 32(10): 2927-2930.