Study on flow distribution of irrigation canal system based on image velocimetry

Abstract

• The YOLOV5 algorithm combined with the monocular ranging principle is used for fast flow measurement of UAV images for UAV hovering type measurement of local river surface flow fields. • Based on the smooth circular arc channel distribution model, the velocity distribution law of Yongji section, Yonggang section and Yonglan section is studied and summarized. • Based on The Rule of Tyson polygon, combined with the representative point of average velocity, the measurement of flow through water section is calculated, and the contactless measurement is realized. With the development of computer technology, it has become possible to measure surface flow velocity using video technology. Ripples are produced when flowing, and flow velocity can be determined based on the ripples and floating objects in water. A UAV velocity measurement system based on optical flow method and YOLOV5 algorithm in deep learning has been established, to realize the monitoring of surface flow field of large rivers. The optical flow method and the deep learning algorithm are used to track the target, convert the pixel distance to actual distance by monocular ranging, and calculate the actual flow velocity. The method was successfully applied to the surface flow field measurement of the Yongji canal in the river-loop irrigation area of Inner Mongolia, and the results showed that the orthophoto graphic images obtained by the method were of high quality, the flow field calculation results were reasonable, and the calculation results agreed well with the measurements.