热带气象学报2024，Vol.40Issue(3)：341-351,11.DOI:10.16032/j.issn.1004-4965.2024.031
基于深度神经网络的台风中心定位方法
A Typhoon Center Location Method Based on Deep Neural Network
摘要
Abstract
Minor errors in typhoon center position can cause significant deviations in typhoon path prediction,so accurately locating typhoon center is an important step in typhoon path prediction and disaster early warning.Typhoon cloud systems change continuously with varying wind strength,leading to diverse and complex satellite images.Existing models based on neural networks are limited in the automatic extraction of typhoon features due to the lack of reasonable weight allocation for multi-dimensional parameters in typhoon images.For this reason,this paper proposed a neural network model(TY-LOCNet)that integrated channel attention and coordinate attention.Firstly,a deep convolutional neural network model was built to extract typhoon characteristics.Secondly,the channel attention mechanism was introduced to capture channel-level information from typhoon characteristics and enhance the attention of the model on important channels.Moreover,the channel attention results were input into the coordinate attention mechanism to calibrate typhoon position information globally so that the model can focus on the morphological structure of typhoons in large areas.Furthermore,the mean square error loss function failed to fuse the calculated coordinates,resulting in low locating accuracy.Therefore,the distance loss function(DISTLoss)was proposed to improve the locating accuracy of the model through distance regression.Experimental results show that the mean location error,mean absolute error,and detection speed of TY-LOCNet were 3.502 pixels,0.292°,and 17 FPS,respectively,outperforming other models.Therefore,the typhoon center location model TY-LOCNet may provide real-time information on typhoon center position for typhoon forecasting.关键词
台风中心定位/注意力机制/神经网络/距离损失函数Key words
typhoon center location/attention mechanism/neural network/distance loss function分类
天文与地球科学引用本文复制引用
郑宗生,沈绪坤,王振华,卢鹏..基于深度神经网络的台风中心定位方法[J].热带气象学报,2024,40(3):341-351,11.基金项目
国家自然科学基金项目(41671431) （41671431）
国家海洋局数字海洋科学技术重点实验室开放基金项目(B201801034) （B201801034）
上海市科委市地方能力建设项目(19050502100) （19050502100）
上海海洋大学科技发展专项基金(A2-2006-20-200211)共同资助 （A2-2006-20-200211）
