The rapidly developed Unmanned Aerial Vehicles (UAV) and artificial intelligence technology has prompted the real-time and accurate observation measurements of tidal bore, the basis of which is tidal bore propagation velocity. In this article, we construct a tidal observation system framework based on UAV and computer vision in order to obtain the tidal bore propagating velocity datasets. Firstly, we focus on the identification of tidal headlines based on the Sobel edge detection, the improved Otsu image segmentation algorithm and the edge connection algorithm with an accuracy of 91%. And then, the detected tidal headlines could be used to control the flight parameters of UAV in order to stably track tidal bore on the specified route with the deviation range below 0.5, and finally to acquire the tidal bore propagation velocity datasets. Comparing with the propagation velocity of the tidal line measured on site, the error of the results is maintained within 0.1 m/s, which demonstrates the effectiveness of our proposed observation method.

中文翻译：

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基于无人机和计算机视觉的潮汐传播速度自动跟踪与智能观测

无人机和人工智能技术的快速发展促进了潮汐实时、准确的观测测量，其基础是潮汐传播速度。在本文中，我们构建了基于无人机和计算机视觉的潮汐观测系统框架，以获得潮汐传播速度数据集。首先，我们重点研究了基于Sobel边缘检测、改进的Otsu图像分割算法和边缘连接算法的潮汐标题识别，准确率达到91%。然后，利用检测到的潮汐标题来控制无人机的飞行参数，从而在指定航线上稳定跟踪潮汐，偏差范围在0.5以下，最终获取潮汐传播速度数据集。与现场测量的潮线传播速度相比，结果误差保持在0.1 m/s以内，这证明了我们提出的观测方法的有效性。