Comparison of two deep learning model YOLOF and Detectron2 for mesoscale eddies identification in the South China Sea

Abstract

ABSTRACT Ocean mesoscale eddy is a special mesoscale phenomenon in the ocean, which widely exists in all oceans and marginal seas around the world. Compared with ordinary ocean circulation, mesoscale eddy has high rotating speed, strong current velocity, and average vertical depths of several kilometres, which play an important role in ocean circulation and material transport. The traditional mesoscale eddy identification and recognition have great subjectivity and usually depends on the parameters predefined or adjusted by experts, which cannot guarantee the accuracy. With the development of artificial intelligence, deep learning method has also been widely used in the detection and recognition of ocean eddies. Based on the sea-level anomaly (SLA) data provided by Copernicus Marine Environment Detection Service, this study compares the results of the ‘you only look once level feature’ (YOLOF) model and Detectron2 model in deep learning on marine mesoscale eddy detection. The mesoscale eddies in the South China Sea from 2011 to 2013 are detected and identified. The accuracy of Detectron2 model is about 57%, while the accuracy of YOLOF model can reach 90%. Comparing the performance of the two models on the same data set, it can be found that YOLOF model improves the accuracy and speed of recognition to a certain extent. The deep learning network model provides an effective technical method for the study of mesoscale eddy detection by sea surface height.