基于计算机视觉的水位检测算法

doi:10.3969/j.issn.0255-8297.2022.03.007

应用科学学报 ›› 2022, Vol. 40 ›› Issue (3): 434-447.doi: 10.3969/j.issn.0255-8297.2022.03.007

基于计算机视觉的水位检测算法

孙维亚¹, 王达², 许帅², 汪京晔², 马占宇²

1. 南水北调中线干线工程建设管理局, 北京 100038;
2. 北京邮电大学人工智能学院, 北京 100088

收稿日期:2021-11-12 发布日期:2022-05-25
通信作者: 马占宇,教授,博导,研究方向为模式识别、机器学习、计算机视觉、非高斯概率模型、贝叶斯网络等。E-mail:mazhanyu@bupt.edu.cn E-mail:mazhanyu@bupt.edu.cn
基金资助:
国家重点研发计划(No.2019YFF0303300,No.2019YFF0303302,No.2020AAA0105200);国家自然科学基金(No.61922015,No.61773071,No.U19B2036);北京市自然科学基金(No.Z200002)资助

Water Level Detection Algorithm Based on Computer Vision

SUN Weiya¹, WANG Da², XU Shuai², WANG Jingye², MA Zhanyu²

1. Construction Administration of the Middle Route of South-to-North Water Diversion Project, Beijing 100038, China;
2. College of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100088, China

Received:2021-11-12 Published:2022-05-25

摘要/Abstract

摘要： 鉴于传统的水位读数方法误差大，成本高，需要一种精确、实时、鲁棒的智能水位检测算法来高效读取水位，为此提出了一种基于计算机视觉的水位检测算法以满足实际需求。首先对拍摄到的图像进行预处理和边缘检测以找出水尺位置，并通过仿射变换对水尺进行矫正。通过两种策略在水尺区域找到水尺关键字的位置，即关键字处理。然后对边缘特征进行投影并检测出水面位置。最后根据关键字处理结果和边缘特征计算得到水面高度。大量实验和实地测试的结果表明：所提算法在基于计算机视觉的水位检测、水尺读数等领域具有理论和应用的双重价值。

关键词: 水尺读数, 计算机视觉, 边缘特征, 关键字处理

Abstract: In view of the problems of high uncertainty and high cost in existing water level meters, this paper presents a set of water level monitoring system based on computer vision to gain high accurate, real-time, robust intelligent water level monitoring. First, preprocessing and edge detection of captured images are carried out to find out the position of the water gauge to be read, and the calibration of the water gauge is carried out by using affine transformation algorithm. Second, the keyword in the water ruler area is positioned and processed by filtering method. Then the edge information is projected to find water surface. Finally, the height of water surface is calculated according to the results of keyword processing and edge information. Experimental verification and field deployment show that the complete water level detection scheme proposed in this paper has both theoretical and practical value in the field of water level detection and water gauge reading based on computer vision.

Key words: water gauge reading, computer vision, edge feature, keyword processing

中图分类号:

TP391.4

孙维亚, 王达, 许帅, 汪京晔, 马占宇. 基于计算机视觉的水位检测算法[J]. 应用科学学报, 2022, 40(3): 434-447.

SUN Weiya, WANG Da, XU Shuai, WANG Jingye, MA Zhanyu. Water Level Detection Algorithm Based on Computer Vision[J]. Journal of Applied Sciences, 2022, 40(3): 434-447.

参考文献

[1] 姜晓玉,花再军.基于图像处理的水位自动读取[J].电子设计工程, 2011, 19(23):23-25. Jiang X Y, Hua Z J. Automatic reading of water level based on image processing[J]. Electronic Design Engineering, 2011, 19(23):23-25.(in Chinese)
[2] 黄战华,熊浩伦,朱猛,等.嵌入式水尺图像检测系统与判读算法研究[J].光电工程, 2013, 40(4):1-7. Huang Z H, Xiong H L, Zhu M, et al. Research on embedded water gauge image detection system and interpretation algorithm[J]. Optoelectronic Engineering, 2013, 40(4):1-7.(in Chinese)
[3] Lopez-Molina C, De Baets B, Bustince H, et al. Multiscale edge detection based on Gaussian smoothing and edge tracking[J]. Knowledge-Based Systems, 2013, 44:101-111.
[4] 吴福朝,胡占义.线性确定无穷远平面的单应矩阵和摄象机自标定[J].自动化学报, 2002, 28(4):488-496. Wu F C, Hu Z Y. Homography matrix for linear determination of infinite plane and camera self-calibration[J]. Chinese Journal of Automation, 2002, 28(4):488-496.(in Chinese)
[5] Nalwa V S, Binford T O. On detecting edges[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, PAMI-8(6):699-714.
[6] Sobel M E. Asymptotic confidence intervals for indirect effects in structural equation models[J]. Sociological Methodology, 1982, 13:290-312.
[7] Mallat S, Hwang W L. Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory, 1992, 38(2):617-643.
[8] Canny J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986(6):679-698.
[9] Hager G D, Belhumeur P N. Efficient region tracking with parametric models of geometry and illumination[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(10):1025-1039.
[10] 曾文锋,李树山,王江安.基于仿射变换模型的图像配准中的平移,旋转和缩放[J].红外与激光工程, 2001, 30(1):18-20. Zeng W F, Li S S, Wang J A. Translation, rotation and scaling in image registration based on affine transformation model[J]. Infrared and Laser Engineering, 2001, 30(1):18-20.(in Chinese)
[11] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017:4700-4708.
[12] 张阳,杜文广.基于计算机视觉的嵌入式水位检控系统设计[J].制造业自动化, 2014, 36(9):121-123 Zhang Y, Du W G. Design of embedded water level inspection and control system based on computer vision[J]. Manufacturing Automation, 2014, 36(9):121-123.(in Chinese)
[13] He K, Sun J, Tang X. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(12):2341-2353.

基于计算机视觉的水位检测算法

Water Level Detection Algorithm Based on Computer Vision

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价

[1]	刘松, 熊邦书. 基于计算机视觉的钢轨焊缝自动定位方法[J]. 应用科学学报, 2019, 37(6): 844-850.
[2]	曹欣, 李战明, 胡文瑾. 一种彩色图像质量评价方法[J]. 应用科学学报, 2019, 37(3): 398-406.
[3]	蔡晨, 潘斌, 刘振宁. 用井字结构光对规则部件进行三维测量[J]. 应用科学学报, 2017, 35(1): 107-116.
[4]	孙贤斌;李德华尹杰熊才权. 机器视觉系统中摄像机的神经网络定标[J]. 应用科学学报, 2007, 25(5): 486-486 .