M-PSPNet多尺度海洋温度锋检测方法

M-PSPNet multi-scale ocean temperature front detection method

海洋温度锋作为一种重要的中尺度海洋现象,是影响海洋热量交换与物质运输以及海气相互作用的关键因素,实现其精准检测是分析海洋锋时空变化及海洋气象动态监测的重要基础。海洋混合、温度变化缓慢导致海洋温度锋具有小目标、弱边缘的特性,针对传统的边缘检测和现有的深度学习方法存在形态刻画不准确和像素误检等问题,本文提出M-PSPNet多尺度海洋温度锋检测方法。该方法通过设计多尺度特征提取模块(Multi-ResNet),在保留浅层学习网络中获得的空间、位置特征的同时,结合深层网络获取的语义特征,提升模型对边缘轮廓、位置信息的检测能力;此外,该方法引入Dice_(loss)与Focal_(loss)组合的混合损失函数DF_(loss),引导模型注重预测结果与标注值的像素级差异,提高锋面像素检测的准确性。为验证方法的有效性,本文基于实验模型设计多组对比实验,实验结果显示:本文M-PSPNet多尺度海洋温度锋检测方法的交并比、查全率、查准率和F1值4项指标分别达到了78.79%、89.59%、86.95%、88.25%,检测效果明显优于对比方法;相比采用ResNet-50模块的模型检测结果,交并比、查全率、F1值3项指标分别提高了14.78%、19.15%、10.13%;相比采用单个损失函数的模型检测结果,交并比、查全率及查准率指标分别提高了1.4%、1.55%、5.1%;对比分析结果表明,本文提出的模型能精准定位海洋温度锋的位置、边缘轮廓,刻画出准确的锋面形态。

As an important mesoscale ocean phenomenon,ocean temperature fronts are a key factor affecting ocean heat exchange,material transport and sea-air interaction.Accurate detection of ocean temperature fronts is crucial for analyzing temporal and spatial changes of ocean temperature fronts and dynamic monitoring of marine meteorological.Due to the ocean mixing and slow temperature change,the ocean temperature front has the characteristics of small targets and weak edges.In view of the problems of inaccurate morphological description and pixel misdetection in traditional edge detection methods and existing deep learning model,this paper proposes a multiscale ocean temperature front detection method,M-PSPNet.The ability of the model to detect edge contours and positional information is improved by designing the multiscale feature extraction module (Multi-ResNet),which retains the spatial and positional features obtained in the shallow learning network while combining them with the semantic features obtained in the deep network.Additionally,the hybrid loss function DF_(loss),which combines Dice_(loss) and Focal_(loss),is introduced to guide the model to focus on the pixel-level difference between the predicted result and the labeled value,improving the accuracy of frontal pixel detection.In order to verify the effectiveness of the proposed method,multiple groups of comparative experiments were designed based on the experimental model.The experimental results show that the M-PSPNet achieves 78.79%,89.59%,86.95%,and 88.25%respectively in IOU,Recall,Precision and F1 score.The model detection results using the Multi-ResNet module increased by 14.78%,19.15%,and 10.13%in IOU,Recall,and F1 score.The model detection results using the DF_(loss) increased by 1.4%,1.55%,and 5.1%in IOU,Recall and Precision.The comparison results demonstrate that the M-PSPNet is capable of accurately locate the position and edge contour of the ocean temperature front,and describe the accurate front shape.