Prototipi značilk za adaptivno zaznavanje ovir na vodni površini

Unmanned surface vehicles (USV) rely on robust perception methods for obstacle detection. Current segmentation-based state-of-the-art methods lack the desired robustness and generalization capabilities required to adapt to new situations. To address this, we design WaSR-AD, a network with an explicit adaptation capability based on class prototypes. Initial prototypes are extracted during training and adapted during inference in an online fashion. The adapted prototypes are used to enrich the image features with additional adaptive context. Evaluation on the MODS benchmark reveals that such explicit adaptation of the prototypes significantly improves the detection performance, achieving 14% lower water segmentation error and 3.6% F1-score increase inside the critical 15m danger-zone area around the boat, with a negligible cost in inference time.