Automatic tracking and intelligent observation of tidal bore propagation velocity based on UAV and computer vision

Abstract

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.