Research on Tidal Level Technology Based on Deep Learning Video Observation: A case study of Xiamen Gaoqi Wharf
2024
Zhi ZENG | Zhijie CHEN | Quan SUN
Nearshore tidal observation is one of the most fundamental tasks in ocean engineering applications, coastal disaster mitigation, coastal zone management, and ocean-related scientific research. In this paper, a method based on video image deep learning is proposed to extract tidal level features from the video frames captured by a fixed camera installed near the shore, using YOLOv5 visual AI model for tidal analysis. The study used the high-definition camera of Xiamen Gaoqi Wharf with a resolution of 1920×1080 as the training and validation dataset for the February 2023, and the test dataset for March 2023. The hourly tide data of the coastal tide verification well is used for annotation, and the YOLOv5 object detection model is used for training. The calculation results show that the errors of tidal observation through video on the training set and the test set are 3.9cm and 5.3cm, respectively. One pixel in the video represents 3.8cm, so the average error of the tidal observation is at the pixel level. The study shows that the method of using high-definition cameras based on image deep learning for tidal observation near the shore is feasible, and the observation accuracy depends on the resolution of the target object in the image.
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