  Ocean fronts can cause the accumulation of nutrients and affect the
propagation of underwater sound, so high-precision ocean front detection is of
great significance to the marine fishery and national defense fields. However,
the current ocean front detection methods either have low detection accuracy or
most can only detect the occurrence of ocean front by binary classification,
rarely considering the differences of the characteristics of multiple ocean
fronts in different sea areas. In order to solve the above problems, we propose
a semantic segmentation network called location and seasonality enhanced
network (LSENet) for multi-class ocean fronts detection at pixel level. In this
network, we first design a channel supervision unit structure, which integrates
the seasonal characteristics of the ocean front itself and the contextual
information to improve the detection accuracy. We also introduce a location
attention mechanism to adaptively assign attention weights to the fronts
according to their frequently occurred sea area, which can further improve the
accuracy of multi-class ocean front detection. Compared with other semantic
segmentation methods and current representative ocean front detection method,
the experimental results demonstrate convincingly that our method is more
effective.
