Semi supervised ocean mesoscale vortex detection method based on feature invariance

Ocean mesoscale eddy detection is an important hotspot of Marine scientific research. Over the last few years, with the development of machine learning research, eddy detection methods based on machine learning have been applied in various fields. However, the traditional scroll detection algorithm has weak generalization ability and low detection accuracy, and the fully supervised scroll detection algorithm needs a large amount of marker data, which is costly and has poor readability. In this paper, a new semi-supervised ocean mesoscale eddy detection method based on feature invariance is proposed. The fully supervised loss calculation model is optimized to solve the problem of serious imbalance of positive and negative samples in loss calculation, so as to achieve the purpose of training the model. In addition, based on the feature invariance, an interpolation consistency calculation method based on flipped image and original image is proposed, which is combined with the consistency method algorithm put forward in CSD networks to increase the precision of detection. Compared with SSD and ISD networks, the proposed meso-scale eddy detection algorithm achieves better performance, with the AP value increasing by 1.7% and 1.1%, respectively.