Abstract
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.
| Original language | English |
|---|---|
| Article number | 8779643 |
| Pages (from-to) | 106336-106345 |
| Number of pages | 10 |
| Journal | IEEE Access |
| Volume | 7 |
| DOIs | |
| State | Published - 2019 |
Keywords
- Eddies detection
- deep neural network
- flow visualization
- object detection