Towards Smaller and Stronger: An Edge-Aware Lightweight Segmentation Approach for Unmanned Surface Vehicles in Water Scenarios.

The accurate detection and segmentation of accessible surface regions in water scenarios is one of the indispensable capabilities of surface unmanned vehicle systems. 'Most existing methods focus on accuracy and ignore the lightweight and real-time demands. Therefore, they are not suitable for embedded devices, which have been wildly applied in practical applications.' An edge-aware lightweight water scenario segmentation method (ELNet), which establishes a lighter yet better network with lower computation, is proposed. ELNet utilizes two-stream learning and edge-prior information. Except for the context stream, a spatial stream is expanded to learn spatial details in low-level layers with no extra computation cost in the inference stage. Meanwhile, edge-prior information is introduced to the two streams, which expands the perspectives of pixel-level visual modeling. The experimental results are 45.21% in FPS, 98.5% in detection robustness, 75.1% in F-score on MODS benchmark, 97.82% in precision, and 93.96% in F-score on USV Inland dataset. It demonstrates that ELNet uses fewer parameters to achieve comparable accuracy and better real-time performance.