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基于机器视觉的闸坝表面位移非接触式监测方法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对闸坝表面位移常规监测方法劳动密度高、监测频次低,且难以实现长期稳定监测等问题,提出一种融合时空特征的闸坝表面位移非接触式智能监测方法.该方法采用人工靶标作为标志物,以摄像机为采集设备,通过无线传输图像信息,利用加权分布的自适应伽马修正(AGCWD)与边缘感知因子改进的加权引导滤波(WGIF)增强低照度图像的特征表达能力,通过计算机搭载基于贝叶斯框架的时空上下文信息(STC)算法深度挖掘靶标图像上下文时空信息,进一步地,引入曲面拟合获取靶标的亚像素级位移时程信息,实现闸坝水平和垂直双向表面位移的亚像素级非接触式监测.实验室与现场试验结果表明,不同实验场景下位移监测数据与校验数据高度一致,误差小于0.05 mm;相比于图像优化处理方法,基于AGCWD与WGIF的图像优化处理方法的峰值信噪比提升2.70％,信息熵增加4.91％,标准差降低2.63％;相比于目标追踪算法,基于曲面拟合的STC目标追踪算法的现场监测数据较同类目标追踪算法精度提升48％,可为闸坝表面位移监测提供高精度的解决方案.
Machine vision-based non-contact monitoring method for gate and dam surface displacements
Aiming at the problems of conventional monitoring method such as high labor density,low monitoring frequency,and difficulty in achieving long-term stable monitoring of the surface displacement of locks and dams,a non-contact intelligent monitoring method integrating spatio-temporal features is proposed.The method adopts an artificial target as a marker,takes a camera as an acquisition device,transmits image information wirelessly,and makes use of Adaptive Gamma Correction Weighted Distribution(AGCWD)and Weighted Guided Image Filter(WGIF)with improved edge-awareness factor.WGIF with edge-awareness factor to enhance the feature expression ability of low illumination images,and the Spatio-Temporal Context(STC)algorithm based on Bayesian framework on board the computer to deeply mine the contextual spatio-temporal information of the target image,and further introduce surface fitting to obtain sub-pixel level displacement information of the target to achieve the sub-pixel displacement information of both horizontal and vertical bi-directional surface displacements of the gate and dam.Further,surface fitting is introduced to obtain subpixel-level displacement information to achieve non-pixel-level non-contact monitoring of horizontal and vertical bi-directional surface displacements of the lock and dam.The la-boratory and field test results show that the displacement monitoring data under different experimental scenarios are highly consistent with the calibration data,and the error is less than 0.05 mm;compared with the existing image optimization processing methods,the image optimization processing methods based on AGCWD and WGIF increase the Peak Signal-to-Noise Ratio(PSNR)by 2.70％,increase the information entropy by 4.91％,and reduce the standard deviation by 2.63％;compared with the established target tracking algorithms,the STC target tracking al-gorithm based on surface fitting is more effective in obtaining sub-pixel-level displacement information.Compared with the existing target tracking algorithms,the field monitoring data of the STC target tracking algorithm based on surface fitting improves the accuracy of the similar target tracking algorithms by 48％,which can provide a high-precision solution for the monitoring of the gate and dam surface displacements.

lock and damsurface displacement monitoringspatio-temporal characteristics of image sequencesdigital image optimisationsub-pixel level target tracking

陈波、何梦佳、刘伟琪、马聪

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河海大学水灾害防御全国重点实验室,江苏南京 210098

河海大学水利水电学院,江苏南京 210098

河海大学水工程安全研究院,江苏南京 210098

闸坝 表面位移监测 图像序列时空特征 数字图像优化 亚像素级目标追踪

国家自然科学基金面上项目国家自然科学基金重点项目全国重点实验室基本科研业务费全国重点实验室基本科研业务费

52079049522390095220122725230248A2

2024

10.13243/j.cnki.slxb.20240008

水利学报
中国水利学会

水利学报

CSTPCD北大核心
影响因子：1.778
ISSN：0559-9350
年,卷(期)：2024.55(9)