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Ocean fronts can cause the accumulation of nutrients and affect thepropagation of underwater sound, so high-precision ocean front detection is ofgreat significance to the marine fishery and national defense fields. However,the current ocean front detection methods either have low detection accuracy ormost can only detect the occurrence of ocean front by binary classification,rarely considering the differences of the characteristics of multiple oceanfronts in different sea areas. In order to solve the above problems, we proposea semantic segmentation network called location and seasonality enhancednetwork (LSENet) for multi-class ocean fronts detection at pixel level. In thisnetwork, we first design a channel supervision unit structure, which integratesthe seasonal characteristics of the ocean front itself and the contextualinformation to improve the detection accuracy. We also introduce a locationattention mechanism to adaptively assign attention weights to the frontsaccording to their frequently occurred sea area, which can further improve theaccuracy of multi-class ocean front detection. Compared with other semanticsegmentation methods and current representative ocean front detection method,the experimental results demonstrate convincingly that our method is moreeffective.

LSENet: Location and Seasonality Enhanced Network for Multiclass Ocean Front Detection