帧差与快速密集光流结合的图像法测流研究

Image Flow Measurement Based on the Combination of Frame Difference and Fast and Dense Optical Flow

图像法测流技术因其简便、高效、安全等优点得到了普遍的关注,开始应用于国内外水文站。目前主流的图像法测流技术主要有大尺度粒子图像测速(large-scale particle image velocimetry,LSPIV)和时空图像测速(spatiotemporal image velocimetry,STIV)等,LSPIV方法采用天然粒子作为示踪物,利用空域互相关法处理图像获得流场矢量,STIV方法根据河流的主流向设定测速线,对视频图像中测速线的灰度进行采样形成时空图像,利用时空图像中的纹理角求得流速。LSPIV方法依赖示踪物的可见性,存在稳定性差等缺点,STIV方法存在对断面流态稳定性要求高和仅能测量1维流速等缺点。本文提出一种结合帧间差分与快速密集光流的分组测流方法(frame difference-fast optical flow using dense inverse search-grouping,FD-DIS-G),利用帧差法计算运动显著性图,通过捕捉细微的水面运动处理河流运动在视频中表现不明显的问题,减少对天然示踪物的依赖,使用快速密集光流法(fast optical flow using dense inverse search,DIS)计算运动显著性图中小块区域之间的密集光流位移,提高流量测量的精度和时效性,有效克服流态不稳定的情况。同时,设计一种分组处理奇异值的方法,提高了算法的整体准确性,增强了算法的稳定性。将流速仪测量得到的垂线平均流速、平均流速以及断面流量作为比测标准,利用水文站所拍得的天然河道水流视频进行比测实验,实验结果表明,相比于目前广泛使用的图像测流方法,本文方法在平均流速和断面流量上的精度有明显的提升,垂线平均流速测量的稳定性有显著的增强且实时性好。

Due to its simplicity,efficiency and safety,image velocimetry has gained widespread attention and is beginning to be used in domestic and international hydrographic stations.At present,the mainstream image-based flow measurement techniques mainly include large-scale particle image velocimetry(LSPIV)and spatio-temporal image velocimetry(STIV),etc.While the LSPIV method uses natural particles as tracers and uses the spatial correlation method to process the image to obtain the flow field vector,the STIV method sets the velocity line according to the main flow direction of the river,samples the grey scale of the velocity line in the video image to form the spatio-temporal image,and uses the texture angle in the spatio-temporal image to obtain the flow velocity.Generally,the LSPIV method has disadvantages such as poor stability due to its reliance on the visibility of the tracer,and the STIV method has disadvantages such as high requirements for the stability of the cross-sectional flow regime and the ability to measure only one-dimensional flow velocities.In this paper,a grouping flow measurement method based on the combination of inter-frame difference and fast optical flow using dense inverse search (FD-DIS-G) was proposed. In the method, the frame difference method was used to calculate a kinematic saliency map that captures subtle water surface motions to deal with the problem that river motions are not evident in the video, which reduces the reliance on natural tracers. Then, the fast optical flow is used to calculate the dense optical flow displacements between small areas of the kinematic prominence map, improving the accuracy and timeliness of flow measurements and effectively overcoming the flow instability. Meanwhile, a method of grouping processing abnormal values was designed, which improves the over-all accuracy of the algorithm and enhances the stability of the algorithm. The results of the experiments showed that the accuracy of the mean flow velocity and cross-sectional flow rate is significantly improved compared with the widely-used image flow measurement methods, and the pro-posed mean flow velocity measurement methods achieves real-time performance and its stability of is significantly enhanced.