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Application Research of Thermal Imaging Technology in Video Image River Flow Velocity Monitoring
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Zhao CAI1, Min-han LIAO2, Jiu-fu LIU1, Hong-wei LIU1, Shao-zhi CHU1, Xing MIN1
Water Resources and Power | 2026, 44(1) : 11 - 14
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Water Resources and Power | 2026, 44(1): 11-14
Application Research of Thermal Imaging Technology in Video Image River Flow Velocity Monitoring
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Zhao CAI1, Min-han LIAO2, Jiu-fu LIU1, Hong-wei LIU1, Shao-zhi CHU1, Xing MIN1
Affiliations
  • 1.Nanjing Hydraulic Research Institute, Nanjing 210029, China
  • 2.Guangzhou Baiyun District and Long Reservoir Management Office, Guangzhou 510540, China
Published: 2026-01-25 doi: 10.20040/j.cnki.1000-7709.2026.20250206
Outline
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The video image flow monitoring technology has been applied in hydrological monitoring and irrigation area monitoring. Due to the limitation of received light, video image flow monitoring method has limitations in monitoring at conditions of night and low light. The thermal imaging was used to increase the spectral applicability of video flow measurement and compensate for the low applicability of video flow measurement under night conditions. By using a visible light and thermal imaging double-sided camera device mounted on a drone, the flow velocity field inversion results of visible light and thermal imaging camera devices under conditions of visible light and night were compared with the flow velocity monitoring data. The results show that under the condition of sufficient visible light during the day, the inversion effect of visible light video image flow velocity is better, and the accuracy error can reach within -5% except for a few monitoring points; Due to the limitation of its resolution, thermal imaging camera has a certain ability to capture natural tracers on the river surface, and has the ability and potential to invert the flow velocity field on the river surface. If missing particles of cold ice are added to the river section, the monitoring accuracy of thermal imaging at night can be significantly improved. Except for one monitoring point, the relative error of monitoring accuracy is 8.4%, and the accuracy of the other three monitoring points is relatively high, all around -1%. The research results can provide important references for improving the monitoring accuracy of video velocity measurement under conditions of nighttime and low light.

video image flow monitoring  /  large scale particle image velocimetry  /  thermal imaging  /  current meter
蔡钊, 廖敏涵, 刘九夫, 刘宏伟, 储少志, 闵星. 热成像技术在视频图像河段流速监测中的应用研究. 水电能源科学, 2026 , 44 (1) : 11 -14 . DOI: 10.20040/j.cnki.1000-7709.2026.20250206
Zhao CAI, Min-han LIAO, Jiu-fu LIU, Hong-wei LIU, Shao-zhi CHU, Xing MIN. Application Research of Thermal Imaging Technology in Video Image River Flow Velocity Monitoring[J]. Water Resources and Power, 2026 , 44 (1) : 11 -14 . DOI: 10.20040/j.cnki.1000-7709.2026.20250206
Year 2026 volume 44 Issue 1
86
9
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Article Info
doi: 10.20040/j.cnki.1000-7709.2026.20250206
  • Receive Date:2025-02-08
  • Online Date:2026-09-28
  • Published:2026-01-25
Article Data
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History
  • Received:2025-02-08
  • Revised:2025-04-10
  • Accepted:2025-04-17
Funding
Affiliations
    1.Nanjing Hydraulic Research Institute, Nanjing 210029, China
    2.Guangzhou Baiyun District and Long Reservoir Management Office, Guangzhou 510540, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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