Research on tidal level recognition algorithm of water gauge image based on depth learning

Chang-kuo Cheng; Hai Guo; Cheng-long Sun; Bin Wang; Ning Wang

doi:10.1117/12.2684956

19 October 2023 Research on tidal level recognition algorithm of water gauge image based on depth learning

Chang-kuo Cheng, Hai Guo, Cheng-long Sun, Bin Wang, Ning Wang

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090N (2023) https://doi.org/10.1117/12.2684956
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Tidal level is very important in transportation, surveying and mapping, fishery and military fields. An automatic tidal level recognition algorithm based on water gauge images was studied to solve the problem that the water gauge data in ocean observation stations cannot be observed and compared with tidal level automatically. The numbers in front of the water line on the water gauge image were extracted by image segmentation technology and recognized by convolutional neural network. Finally, the automatic measurement of the tidal level of the water gauge is realized with 91.1% recognition success rate which promotes the unmanned and intelligent process of the ocean observation stations.

Citation Download Citation

Chang-kuo Cheng, Hai Guo, Cheng-long Sun, Bin Wang, and Ning Wang "Research on tidal level recognition algorithm of water gauge image based on depth learning", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090N (19 October 2023); https://doi.org/10.1117/12.2684956

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