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Overview of Vision-based Velocity Measurement Techniques in Trunk Canals
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Di FAN, Yu-hui ZHOU, Hui-min SUN, Yi-ming LI, Xiao-jie WU, A-ying WEI*
Science Technology and Engineering | 2025, 25(19) : 7897 - 7908
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Science Technology and Engineering | 2025, 25(19): 7897-7908
Surveies∙Automation and Computational Technology
Overview of Vision-based Velocity Measurement Techniques in Trunk Canals
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Di FAN, Yu-hui ZHOU, Hui-min SUN, Yi-ming LI, Xiao-jie WU, A-ying WEI*
Affiliations
  • College of Electronics and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Published: 2025-07-08 doi: 10.12404/j.issn.1671-1815.2404998
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The velocity measurement of trunk canals and rivers is regarded as an important basis for water resources management and flood prediction. The techniques and methods for trunk canals velocity measurement based on machine vision were analyzed and synthesized. Particular focus was placed on reviewing the principles, technologies, and recent developments of particle image velocimetry, particle tracking velocimetry, space-time image velocimetry, optical flow methods, and deep learning-based flow measurement methods in recent years. Finally, the existing challenges and issues were addressed, and potential future development directions were proposed.

machine vision  /  velocity measurement of trunk canal  /  deep learning  /  particle image velocimetry  /  space-time image velocimetry
Di FAN, Yu-hui ZHOU, Hui-min SUN, Yi-ming LI, Xiao-jie WU, A-ying WEI. Overview of Vision-based Velocity Measurement Techniques in Trunk Canals[J]. Science Technology and Engineering, 2025 , 25 (19) : 7897 -7908 . DOI: 10.12404/j.issn.1671-1815.2404998
Year 2025 volume 25 Issue 19
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Article Info
doi: 10.12404/j.issn.1671-1815.2404998
  • Receive Date:2024-07-04
  • Online Date:2025-12-22
  • Published:2025-07-08
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  • Received:2024-07-04
  • Revised:2025-01-24
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    College of Electronics and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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