海洋科学中尺度涡的计算机视觉检测和分析方法

Computer Vision Detection and Analysis of Mesoscale Eddies in Marine Science

【目的】中尺度涡是海洋科学领域一个重要的研究课题,在人工智能领域中深度神经网络的技术支持下,将计算机视觉与海洋领域相结合,促进中尺度涡智能识别技术的应用和发展。【方法】基于Mask-RCNN算法,将深度学习中的目标检测算法应用于中尺度涡检测,结合多模态卫星遥感图像数据,对海洋中的中尺度涡进行识别、分类和分割。【结果】传统的中尺度涡检测方法只能检测中尺度涡的位置和大小,本文算法利用多模态信息进行定位、分类和实例分割,有效提取中尺度涡的特征,可以获得更高的检测准确率。【结论】人工智能在中尺度涡识别和追踪问题上具有重大的应用潜力,但涡旋检测的评价指标仍需进一步完善,促进深度学习检测中尺度涡技术向快速高效的方向发展。

[Objective]Mesoscale eddy is an important research topic in the field of marine science.With the support of deep neural network in the artificial intelligence field,computer vision is combined with ocean field technology to promote the application and development of intelligent recognition technology of mesoscale eddy.[Methods]Based on Mask-RCNN algorithm,a deep learning object detection algorithm is applied to the detection of mesoscale eddies.Combined with the multi-modal satellite remote sensing image data,the mesoscale eddies in the ocean can be identified,classified and segmented.[Results]The traditional method can only detect the location and size of mesoscale eddies.In this paper,multi-modal information is adopted to locate,classify and segment the mesoscale eddies.[Conclusions]Artificial intelligence has great application potential in recognizing and tracking mesoscale eddies.But the evaluation indexes of eddy detection still need to be further improved to promote the rapid and efficient development of deep learning detection of mesoscale eddies.