基于深度神经网络的台风中心定位方法OA北大核心CSTPCD

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.