A Streampath-Based RCNN Approach to Ocean Eddy Detection

IEEE Access

Changbo Wang¹  Xue Bai¹  Chenhui Li¹ 

[1] School of Computer Science and Technology, East China Normal University, Shanghai, China;

关键词: Eddies detection;    deep neural network;    flow visualization;    object detection;   

DOI  :  10.1109/ACCESS.2019.2931781

来源: DOAJ

【 摘 要 】

An eddy is a circular current of water in the ocean that affects the fields of maritime transport, ocean data analysis, and so on. Traditional eddy detection methods are based on numerical simulation data and satellite images and their accuracy is affected greatly by manual threshold adjustment. In this paper, we present a new eddy detection approach via deep neural networks to improve eddy detection accuracy. First, we present a streampath-based approach to build a large-scale eddy image dataset from ocean current data and apply our dataset to eddy detection. Second, by combining the multilayer features in the neural network with the characteristics of the eddies, we achieve a competitive detection result with an mAP of 90.64% and an average SDR of 98.91%, which performs better than the previous methods. Third, through our enhanced eddy visualization approach, we solve the problem that eddies are difficult to detect in the sparse streampath region.

【 授权许可】

Unknown