ThinkRiver: Semantic verification for water surface object detection

Automated cleaning of floating garbage is a critical component of AI-empowered environmental monitoring for sustainable Smart Cities. Intelligent mobile sensing platforms, such as Unmanned Surface Vehicles (USVs), serve as key IoT nodes for aquatic ecosystem maintenance. However, existing detectors often misidentify natural debris as garbage under complex water conditions, a challenge known as visual mimicry. This study proposes ThinkRiver, a Coarse-to-Fine Semantic Verification Framework designed to enhance the sensing reliability of IoT devices by integrating a high-recall YOLOv8s proposer with a lightweight multimodal system, Qwen3-VL. By conducting semantic verification through Explicit Attribute Reasoning, the system enables more intelligent decision-making within the sensing network. Experiments on an Out-of-Distribution Challenge Set show that ThinkRiver reduces false positives from 261 to 82 (a 68.6% decrease) and improves Precision by 23.76%. These results indicate that semantic-reasoning-enhanced sensors can significantly strengthen the reliability of detection systems in dynamic and visually ambiguous environments, supporting the deployment of scalable AI-IoT solutions.