Recognition of wave characteristics in dredging construction area based on visual information

Abstract

For the process of wave detection in sea area, the traditional buoy monitoring spatial distribution is uneven, and satellite remote sensing is time-consuming and unfavorable to the safety of dredging construction. In this paper, the video/image feature detection method based on visual information, using UAV to obtain video information of the sea environment and automatically extracting wave features through convolutional networks, it can achieve the purpose of inferring regional wave changes from a single image. The network model combines a lightweight Mobilenet-V2 deep convolutional network in the network architecture, based on deep separable convolution and superimposed residual connections, which has the advantages of fast speed and high accuracy, and can realize real-time computation at the shipboard terminal and the network requires less additional computational resources, which reduces the need for energy supply and facilitates the deployment of large-scale dredging projects. The experimental results show that the network identifies 89% of samples with wave height error less than 20% and 94% of periods, which has strong robustness and good practicality in the field of ship dredging engineering.