LSENet: Location and Seasonality Enhanced Network for Multi-Class Ocean Front Detection

TL;DR

This paper introduces LSENet, a semantic segmentation network that enhances multi-class ocean front detection accuracy by integrating seasonality and location attention mechanisms, outperforming existing methods.

Contribution

The paper presents a novel LSENet architecture with channel supervision and location attention modules for pixel-level multi-class ocean front detection.

Findings

LSENet achieves higher detection accuracy than existing methods.

The location attention mechanism improves class-specific detection.

Experimental results validate the effectiveness of the proposed approach.

Abstract

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.