Data-attention-YOLO (DAY): A comprehensive framework for mesoscale eddy identification

Abstract

The accurate mesoscale eddy identification methods with deep learning framework depend on either single eddy characteristic from altimeter missions or multi-step eddy examination strategies, disregarding those indistinguishable features from multiple eddy data integration. In this article, we first propose a data-attention-based YOLO (DAY) to precisely recognize mesoscale eddies in the South China Sea (SCS), which can hierarchically unite multiple eddy attributes and efficiently predict eddies with one-step strategy involving detection and classification. It consists of two main components: heterogeneous eddy data integration module and dynamic attention detecting module for eddy identification. The data integration component empirically transforms the field of multi-source eddy data and propagates eddy labels through automatic labeling method, which sustains a good supply for our dynamic attention-base detecting network. To thoroughly identify mesoscale eddies based on spatio-temporal patterns, DAY efficiently learns the characteristics of mesoscale eddies with an improved one-step identification YOLO network. The comparative evaluation results demonstrate that DAY achieves 54% performance improvement over the state-of-the-art methods on single gray SLA data and outperforms two-stage detecting technique Faster R-CNN by 51%.