标准双色水尺的图像法水位测量
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摘要
图像法水位测量利用图像处理技术自动检测水位线并识别水尺读数,具有非接触测量、无温漂、结果可追溯、系统造价低等优点。然而受到户外视频监控系统在拍摄视角倾斜、成像分辨率低、光照条件复杂等方面的制约,水尺刻度及字符的检测和识别实际上存在较大困难,导致现有方法的稳定性和适宜性差。针对上述问题,从摄影测量的角度提出了一种解决方案。首先根据标准双色水尺的样式设计模板图像;然后通过人工选取的控制点建立感兴趣区域和模板图像间的透视投影变换关系,将水尺图像配准到正射坐标系下;接下来根据配准图像中设置的采样窗口计算自适应分割阈值将其二值化;最终在二值图像的水平投影曲线中检测水位线,并根据模板图像的物理分辨率将其坐标换算为水位测量值。在不同条件下开展了4组现场试验。结果表明,本方法对于日夜光照下拍摄的低分辨率图像均具有较好的鲁棒性,测量分辨率和精度分别达到1 mm和1 cm,满足水文测验的要求。
Image-based water level measurement method automatically detects the water line and recognizes its reading on the staff gauge with image processing technique. It has the advantages such as non-contact measurement, no temperature drift, traceable result, low system cost and etc. However, limited by tilt shooting angle, low imaging resolution and complex illumination in outdoor video surveillance systems, the detection and recognition of scales and characters on the staff gauge are quite difficult in practice, resulting in poor stability and suitability of the existing methods. Aiming at these problems, a solution is proposed from the perspective of photogrammetry. Firstly, a template image is designed according to the style of standard bicolor staff gauge. Secondly, the perspective projection transformation relationship between the region of interest(ROI) and the template image is established through manually selected control points, then the staff gauge image is registrated to orthographic coordinate system. Thirdly, according to the sampling windows set in the registration image the adaptive segmentation threshold is calculated and binarized. Finally, the water line is detected in the horizontal projection curve of the binary image, its coordinates are converted to the water level measurement value according to the physical resolution of the template image. Four sets of field tests were conducted under different conditions. Results show that the method is robust for the low resolution images recorded under both day and night illuminations. The measurement resolution and accuracy achieve 1 mm and 1 cm, respectively, which meets the hydrometry requirements.
Image-based water level measurement method automatically detects the water line and recognizes its reading on the staff gauge with image processing technique. It has the advantages such as non-contact measurement, no temperature drift, traceable result, low system cost and etc. However, limited by tilt shooting angle, low imaging resolution and complex illumination in outdoor video surveillance systems, the detection and recognition of scales and characters on the staff gauge are quite difficult in practice, resulting in poor stability and suitability of the existing methods. Aiming at these problems, a solution is proposed from the perspective of photogrammetry. Firstly, a template image is designed according to the style of standard bicolor staff gauge. Secondly, the perspective projection transformation relationship between the region of interest(ROI) and the template image is established through manually selected control points, then the staff gauge image is registrated to orthographic coordinate system. Thirdly, according to the sampling windows set in the registration image the adaptive segmentation threshold is calculated and binarized. Finally, the water line is detected in the horizontal projection curve of the binary image, its coordinates are converted to the water level measurement value according to the physical resolution of the template image. Four sets of field tests were conducted under different conditions. Results show that the method is robust for the low resolution images recorded under both day and night illuminations. The measurement resolution and accuracy achieve 1 mm and 1 cm, respectively, which meets the hydrometry requirements.
引文
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[2] 张应辉. 浅谈山区型河流水位计的选型[J]. 水利水文自动化, 2008(4):45-46.ZHANG Y H. A brief discussion on model selection of water level gauge for mountain river[J]. Automation in Water Resources and Hydrology, 2008(4):45-46.
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[11] 张振, 徐枫, 沈洁, 等. 基于变高单应的单目视觉平面测量方法[J]. 仪器仪表学报, 2014, 35(8): 1860-1868.ZHANG ZH, XU F, SHEN J, et al. Plane measurement method with monocular vision based on variable-height homography[J]. Chinese Journal of Scientific Instrument, 2014, 35(8): 1860-1868.
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[14] 林瑞凤, 徐海. 基于图像传感器的明渠水位自动测量方法[J]. 传感器与微系统, 2013, 32(8):53-55.LIN R F, XU H. Automatic measurement method for canals water level based on imaging sensor[J]. Transducer and Microsystem Technologies, 2013, 32(8):53-55.
[15] 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 ZH H, XIONG H L, ZHU M, et al. Embedded measurement system and interpretation algorithm for water gauge image[J]. Opto-Electronic Engineering, 2013, 40(4):1-7.
[16] 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. Water-Level auto reading based on image processing[J]. Electronic Design Engineering, 2011, 19(23):23-25.
[17] 石玉立, 夏振, 王林. 基于IDL的视频图像水位检测新算法[J]. 科学技术与工程, 2014, 14(29):114-116.SHI Y L, XIA ZH, WANG L. A new algorithm of water level detection based on video image[J]. Science Technology and Engineering, 2014, 14(29):114-116.
[18] LIN F, CHANG W Y, LEE L C, et al. Applications of image recognition for real-time water level and surface velocity[C]. IEEE International Symposium on Multimedia, 2014:259-262.
[19] 兰华勇, 严华. 基于图像识别技术的水尺刻度提取方法研究[J]. 人民黄河, 2015, 37(3):28-30.LAN H Y, YAN H. Research on application of the scale extraction of water-level ruler based on image recognition technology[J]. Yellow River, 2015, 37(3):28-30.
[20] 陈翠, 刘正伟, 陈晓生,等. 基于图像处理的水位信息自动提取技术[J]. 水利信息化, 2016(1):48-55.CHEN C, LIU ZH W, CHEN X SH, et al. Technology of water level automatically extract based on image processing[J]. Water Resources Informatization, 2016(1):48-55.
[21] KIM J D, HAN Y J, HAHN H S. Embedded implementation of image-based water-level measurement system [J]. IET Computer Vision, 2011, 5(2):125-133.
[22] 李翊, 兰华勇, 严华. 基于图像处理和BP神经网络的水位识别研究[J]. 人民黄河, 2015, 37(12):12-15.LI Y, LAN H Y, YAN H. Research on water-level recognition based on image processing and BP artificial neural network technology[J]. Yellow River, 2015, 37(3):28-30.
[23] 陈金水. 基于视频图像识别的水位数据获取方法[J]. 水利信息化, 2013(1):48-51.CHEN J SH. Method of water level data capturing based on video image recognition[J]. Water Resources Informatizatio,. 2013(1):48-51.
[24] NGUYEN L S, SCHAELI B, SAGE D, et al. Vision-based system for the control and measurement of wastewater flow rate in sewer systems.[J]. Water Science & Technology, 2009, 60(9):2281.
[25] GILMORE T E, BIRGAND F, CHAPMAN K W. Source and magnitude of error in an inexpensive image-based water level measurement system[J]. Journal of Hydrology, 2013, 496(496):178-186.
[2] 张应辉. 浅谈山区型河流水位计的选型[J]. 水利水文自动化, 2008(4):45-46.ZHANG Y H. A brief discussion on model selection of water level gauge for mountain river[J]. Automation in Water Resources and Hydrology, 2008(4):45-46.
[3] 徐立中, 张振, 严锡君, 等. 非接触式明渠水流监测技术的发展现状[J]. 水利信息化, 2013 (3): 37-44.XU L ZH, ZHANG ZH, YAN X J, et al. Advances of non-contact instruments and techniques for open-channel flow measurements[J]. Water Resources Informatization, 2013(3): 37-44.
[4] JODEAU M, HAUET A, PAQUIER A, et al. Application and evaluation of LS-PIV technique for the monitoring of river surface velocities in high flow conditions[J]. Flow Measurement and Instrumentation, 2008, 19(2): 117-127.
[5] LE COZ J, HAUET A, PIERREFEU G, et al. Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in mediterranean rivers[J]. Journal of Hydrology, 2010, 394(1): 42-52.
[6] LEE M C, LEU J M, CHAN H C, et al. The measurement of discharge using a commercial digital video camera in irrigation canals[J]. Flow Measurement and Instrumentation, 2010, 21(2): 150-154.
[7] TSUBAKI R, FUJITA I, TSUTSUMI S. Measurement of the flood discharge of a small-sized river using an existing digital video recording system[J]. Journal of Hydro-environment Research, 2011, 5(4): 313-321.
[8] DRAMAIS G, LE COZ J, CAMENEN B, et al. Advantages of a mobile LSPIV method for measuring flood discharges and improving stage-discharge curves[J]. Journal of Hydro-environment Research, 2011, 5(4): 301-312.
[9] 张振,徐枫,王鑫,等. 河流水面成像测速研究进展[J]. 仪器仪表学报, 2015, 36(7): 1441-1450.ZHANG ZH, XU F,WANG X, et al. Research progress on river surface imaging velocimetry [J]. Chinese Journal of Scientific Instrument, 2015, 36(7): 1441-1450.
[10] STUMPF A, AUGEREAU E, DELACOURT C, et al. Photogrammetric discharge monitoring of small tropical mountain rivers: A case study at Rivière des Pluies, Rèunion island[J]. Water Resources Research, 2016, 52(6):WR018292.
[11] 张振, 徐枫, 沈洁, 等. 基于变高单应的单目视觉平面测量方法[J]. 仪器仪表学报, 2014, 35(8): 1860-1868.ZHANG ZH, XU F, SHEN J, et al. Plane measurement method with monocular vision based on variable-height homography[J]. Chinese Journal of Scientific Instrument, 2014, 35(8): 1860-1868.
[12] 张振,吕莉,石爱业,等. 基于物像尺度变换的河流水面流场定标方法[J]. 仪器仪表学报, 2017, 38(9): 2273-2281.ZHANG ZH, LV L, SHI AI Y, et al. A river surface flow field calibration method based on object-image scaling [J]. Chinese Journal of Scientific Instrument, 2017, 38(9): 2273-2281.
[13] 任明武, 杨万扣, 王欢, 等. 一种基于图像的水位自动测量新方法[J]. 计算机工程与应用, 2007, 43(22):204-206.REN M W, YANG W K, WANG H, et al. New algorithm of automatic water level measurement based on image processing[J]. Computer Engineering and Applications. 2007, 43(22):204-206.
[14] 林瑞凤, 徐海. 基于图像传感器的明渠水位自动测量方法[J]. 传感器与微系统, 2013, 32(8):53-55.LIN R F, XU H. Automatic measurement method for canals water level based on imaging sensor[J]. Transducer and Microsystem Technologies, 2013, 32(8):53-55.
[15] 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 ZH H, XIONG H L, ZHU M, et al. Embedded measurement system and interpretation algorithm for water gauge image[J]. Opto-Electronic Engineering, 2013, 40(4):1-7.
[16] 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. Water-Level auto reading based on image processing[J]. Electronic Design Engineering, 2011, 19(23):23-25.
[17] 石玉立, 夏振, 王林. 基于IDL的视频图像水位检测新算法[J]. 科学技术与工程, 2014, 14(29):114-116.SHI Y L, XIA ZH, WANG L. A new algorithm of water level detection based on video image[J]. Science Technology and Engineering, 2014, 14(29):114-116.
[18] LIN F, CHANG W Y, LEE L C, et al. Applications of image recognition for real-time water level and surface velocity[C]. IEEE International Symposium on Multimedia, 2014:259-262.
[19] 兰华勇, 严华. 基于图像识别技术的水尺刻度提取方法研究[J]. 人民黄河, 2015, 37(3):28-30.LAN H Y, YAN H. Research on application of the scale extraction of water-level ruler based on image recognition technology[J]. Yellow River, 2015, 37(3):28-30.
[20] 陈翠, 刘正伟, 陈晓生,等. 基于图像处理的水位信息自动提取技术[J]. 水利信息化, 2016(1):48-55.CHEN C, LIU ZH W, CHEN X SH, et al. Technology of water level automatically extract based on image processing[J]. Water Resources Informatization, 2016(1):48-55.
[21] KIM J D, HAN Y J, HAHN H S. Embedded implementation of image-based water-level measurement system [J]. IET Computer Vision, 2011, 5(2):125-133.
[22] 李翊, 兰华勇, 严华. 基于图像处理和BP神经网络的水位识别研究[J]. 人民黄河, 2015, 37(12):12-15.LI Y, LAN H Y, YAN H. Research on water-level recognition based on image processing and BP artificial neural network technology[J]. Yellow River, 2015, 37(3):28-30.
[23] 陈金水. 基于视频图像识别的水位数据获取方法[J]. 水利信息化, 2013(1):48-51.CHEN J SH. Method of water level data capturing based on video image recognition[J]. Water Resources Informatizatio,. 2013(1):48-51.
[24] NGUYEN L S, SCHAELI B, SAGE D, et al. Vision-based system for the control and measurement of wastewater flow rate in sewer systems.[J]. Water Science & Technology, 2009, 60(9):2281.
[25] GILMORE T E, BIRGAND F, CHAPMAN K W. Source and magnitude of error in an inexpensive image-based water level measurement system[J]. Journal of Hydrology, 2013, 496(496):178-186.