智能无人船融合AI与传感技术，实现实时水质与塑料垃圾监测。
Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring
- 基于双目相机和YOLOv5模型识别水面宏观塑料。
- 在塞维利亚大湖完成实验，成功检测水体参数与塑料分布。
- 适用于智慧环保、水域巡检等实际场景的AI部署平台。
配备水质传感器和人工智能视觉系统的自主水面车辆，可实现水资源环境监测的智能化与自适应部署。本文展示了一款真实原型车，集成高精度传感器以测量水质参数和水深，并通过双目相机结合深度视觉模型（如YOLOv5）在真实环境中检测并定位宏观塑料。实验在西班牙塞维利亚的Lago Mayor开展，结果验证了该架构的有效性。整体系统及初步成果为水资源监测提供了可靠平台，也为路径规划、人工智能视觉等算法的实际部署提供了真实应用场景。
原文摘要 · Abstract (English)
The use of Autonomous Surface Vehicles, equipped with water quality sensors and artificial vision systems, allows for a smart and adaptive deployment in water resources environmental monitoring. This paper presents a real implementation of a vehicle prototype that to address the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. Furthermore, by means of a stereo-camera, it also can detect and locate macro-plastics in real environments by means of deep visual models, such as YOLOv5. In this paper, experimental results, carried out in Lago Mayor (Sevilla), has been presented as proof of the capabilities of the proposed architecture. The overall system, and the early results obtained, are expected to provide a solid example of a real platform useful for the water resource monitoring task, and to serve as a real case scenario for deploying Artificial Intelligence algorithms, such as path planning, artificial vision, etc.
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