Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TCLNet: Learning to Locate Typhoon Center Using Deep Neural Network   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.

27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
2
The categories of this paper are: math
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg