一种基于机载深度推理的水表污染物监测方法

Method for Monitoring Water Meter Pollutants Based on Airborne Depth Reasoning

水表塑料污染被证明是一个全球性问题,引起了全世界越来越多的关注。如何实时监测水面塑料废弃物,准确收集分析相关数值数据,已成为水环境研究的热点。(1)背景:近年来,无人机(UAV)被逐步采用,用于对水面塑料废物的监测进行研究。从整体上看,被监控的数据被存储在无人机中,以便随后进行检索和分析,从而可能导致实时信息的丢失,阻碍整个监控过程无法完全自动化。(2)方法:对水面系统中各类塑料废弃物的关系、功能及相关机理进行调查。在此基础上,本研究构建了基于深度学习的轻质水面塑料垃圾检测模型,能够自动检测和定位不同的水面塑料垃圾。此外,还构建了基于无人机平台的边缘计算架构。(3)结果:有效降低了返回任务数据的延迟和无人机能耗,并优化了计算和网络资源的分配。(4)结论:基于机载深度推理的无人机平台有望成为未来水环境监测的主流手段。

Water surface plastic pollution turns out to be a global issue,which causes increasing concern worldwide.How to monitor water surface plastic waste in real time and accurately collect and analyze the relevant numerical data has become a hotspot in water environment research.Over the past few years,unmanned aerial vehicles(UAVs) have been progressively adopted to conduct studies on the monitoring of water surface plastic waste.On the whole,the monitored data are stored in the UAVS to be subsequently retrieved and analyzed,thereby probably causing the loss of real-time information and hindering the whole monitoring process from being fully automated.Based on the investigation of the relationship,function and relevant mechanism between various types of plastic waste in the water surface system.this study builds a deep learning-based lightweight water surface plastic waste detection model,which is capable of automatically detecting and locating different water surface plastic waste.Moreover,a UAV platform-based edge computing framework is built.Results show that the delay of return task data and UAV energy consumption is effectively reduced,and computing and network resources are optimally allocated.Therefore,the UAV platform based on airborne depth reasoning is expected to be the mainstream means of water environment monitoring in the future.