  The perception of waterways based on human intent is significant for
autonomous navigation and operations of Unmanned Surface Vehicles (USVs) in
water environments. Inspired by visual grounding, we introduce WaterVG, the
first visual grounding dataset designed for USV-based waterway perception based
on human prompts. WaterVG encompasses prompts describing multiple targets, with
annotations at the instance level including bounding boxes and masks. Notably,
WaterVG includes 11,568 samples with 34,987 referred targets, whose prompts
integrates both visual and radar characteristics. The pattern of text-guided
two sensors equips a finer granularity of text prompts with visual and radar
features of referred targets. Moreover, we propose a low-power visual grounding
model, Potamoi, which is a multi-task model with a well-designed Phased
Heterogeneous Modality Fusion (PHMF) mode, including Adaptive Radar Weighting
(ARW) and Multi-Head Slim Cross Attention (MHSCA). Exactly, ARW extracts
required radar features to fuse with vision for prompt alignment. MHSCA is an
efficient fusion module with a remarkably small parameter count and FLOPs,
elegantly fusing scenario context captured by two sensors with linguistic
features, which performs expressively on visual grounding tasks. Comprehensive
experiments and evaluations have been conducted on WaterVG, where our Potamoi
archives state-of-the-art performances compared with counterparts.
