论文标题
TCLNET：学习使用深神经网络来定位台风中心
TCLNet: Learning to Locate Typhoon Center Using Deep Neural Network
论文作者
论文摘要
台风中心位置的任务在台风强度分析和台风路径预测中起着重要作用。传统的台风中心位置算法主要依赖数字图像处理和数学形态操作，这些操作实现了有限的性能。在本文中，我们提出了一个名为TCLNET的有效的完全卷积的端到端深神经网络，以自动定位台风中心位置。我们仔细设计网络结构，以便我们的TCLNET可以在其轻质体系结构上实现出色的性能基础。此外，我们还提供了一个全新的大型台风中心位置数据集（TCLD），以便可以以有监督的方式对TCLNET进行培训。此外，我们建议使用新型的TCL+分段损耗函数来进一步提高TCLNET的性能。广泛的实验结果和比较证明了我们的模型的性能，与基于SOTA深度学习的台风中心位置方法相比，参数降低了92.7％，我们的TCLNET的准确性提高了14.4％。
The task of typhoon center location plays an important role in typhoon intensity analysis and typhoon path prediction. Conventional typhoon center location algorithms mostly rely on digital image processing and mathematical morphology operation, which achieve limited performance. In this paper, we proposed an efficient fully convolutional end-to-end deep neural network named TCLNet to automatically locate the typhoon center position. We design the network structure carefully so that our TCLNet can achieve remarkable performance base on its lightweight architecture. In addition, we also present a brand new large-scale typhoon center location dataset (TCLD) so that the TCLNet can be trained in a supervised manner. Furthermore, we propose to use a novel TCL+ piecewise loss function to further improve the performance of TCLNet. Extensive experimental results and comparison demonstrate the performance of our model, and our TCLNet achieve a 14.4% increase in accuracy on the basis of a 92.7% reduction in parameters compared with SOTA deep learning based typhoon center location methods.
