{"title":"视频流速测量:基于深度学习的两阶段流速预测方法","authors":"Xiaolong Wang, Qiang Ma, Genyi Wang, Guocheng An","doi":"10.2166/nh.2024.128","DOIUrl":null,"url":null,"abstract":"<p>Due to the uncertainty in output caused by environmental changes, significant discrepancies are expected between the surface flow velocities predicted using deep learning methods and the instantaneous flow velocities. In this paper, a two-stage deep learning flow velocity measurement algorithm is proposed. During the external calibration process, the upper and lower frames of the recorded water flow video are cyclically traversed to acquire predicted flow velocity values using the deep learning velocity measurement algorithm. Meanwhile, the pixel displacement is obtained using the sparse optical flow tracking method and then post-processed to derive the velocity calibration value and pixel calibration value. During the detection process, the deep learning-predicted flow velocity is internally calibrated using the velocity calibration value and the pixel calibration value to adapt to changes in water flows. Compared with the pre-improved algorithm, the method achieves the minimum root mean square error in five different flow velocity videos and maintains high accuracy when the flow velocity changes rapidly. The obtained results are very promising and can help improve the reliability of video flow rate assessment algorithms.</p>","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Video velocity measurement: A two-stage flow velocity prediction method based on deep learning\",\"authors\":\"Xiaolong Wang, Qiang Ma, Genyi Wang, Guocheng An\",\"doi\":\"10.2166/nh.2024.128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Due to the uncertainty in output caused by environmental changes, significant discrepancies are expected between the surface flow velocities predicted using deep learning methods and the instantaneous flow velocities. In this paper, a two-stage deep learning flow velocity measurement algorithm is proposed. During the external calibration process, the upper and lower frames of the recorded water flow video are cyclically traversed to acquire predicted flow velocity values using the deep learning velocity measurement algorithm. Meanwhile, the pixel displacement is obtained using the sparse optical flow tracking method and then post-processed to derive the velocity calibration value and pixel calibration value. During the detection process, the deep learning-predicted flow velocity is internally calibrated using the velocity calibration value and the pixel calibration value to adapt to changes in water flows. Compared with the pre-improved algorithm, the method achieves the minimum root mean square error in five different flow velocity videos and maintains high accuracy when the flow velocity changes rapidly. The obtained results are very promising and can help improve the reliability of video flow rate assessment algorithms.</p>\",\"PeriodicalId\":13096,\"journal\":{\"name\":\"Hydrology Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Hydrology Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.2166/nh.2024.128\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"WATER RESOURCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hydrology Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.2166/nh.2024.128","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"WATER RESOURCES","Score":null,"Total":0}
Video velocity measurement: A two-stage flow velocity prediction method based on deep learning
Due to the uncertainty in output caused by environmental changes, significant discrepancies are expected between the surface flow velocities predicted using deep learning methods and the instantaneous flow velocities. In this paper, a two-stage deep learning flow velocity measurement algorithm is proposed. During the external calibration process, the upper and lower frames of the recorded water flow video are cyclically traversed to acquire predicted flow velocity values using the deep learning velocity measurement algorithm. Meanwhile, the pixel displacement is obtained using the sparse optical flow tracking method and then post-processed to derive the velocity calibration value and pixel calibration value. During the detection process, the deep learning-predicted flow velocity is internally calibrated using the velocity calibration value and the pixel calibration value to adapt to changes in water flows. Compared with the pre-improved algorithm, the method achieves the minimum root mean square error in five different flow velocity videos and maintains high accuracy when the flow velocity changes rapidly. The obtained results are very promising and can help improve the reliability of video flow rate assessment algorithms.
期刊介绍:
Hydrology Research provides international coverage on all aspects of hydrology in its widest sense, and welcomes the submission of papers from across the subject. While emphasis is placed on studies of the hydrological cycle, the Journal also covers the physics and chemistry of water. Hydrology Research is intended to be a link between basic hydrological research and the practical application of scientific results within the broad field of water management.