School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.
School of Automation, China University of Geosciences, Wuhan, 430074, China.
Environ Sci Pollut Res Int. 2019 Jan;26(3):2722-2733. doi: 10.1007/s11356-018-3645-z. Epub 2018 Nov 27.
Water environment monitoring is of great importance to human health, ecosystem sustainability, and water transport. Unlike traditional water quality monitoring problems, this paper focuses on visual perception of water environment. We first introduce the development of a customized aquatic sensor node equipped with an embedded camera sensor. Based on this platform, we present an efficient and holistic contamination detection approach, which can automatically adapt to the detection of floating debris in dynamic waters or the identification of salient regions in static waters. Our approach is specifically designed based on compressed sensing theory to give full consideration to the unique challenges in water environment and the resource constraints on sensor nodes. Both laboratory and field experiments demonstrate the proposed method can fast and accurately detect various types of water pollutants and is a better choice for camera sensor-based water environment monitoring compared with other methods.
水环境监测对于人类健康、生态系统可持续性和水运输都具有重要意义。与传统的水质监测问题不同,本文专注于水环境的视觉感知。我们首先介绍了一种定制化的水生传感器节点的开发,该节点配备了嵌入式摄像头传感器。基于这个平台,我们提出了一种高效、全面的污染检测方法,该方法可以自动适应动态水域中漂浮物的检测或静态水域中显著区域的识别。我们的方法是专门基于压缩感知理论设计的,充分考虑了水环境的独特挑战和传感器节点的资源限制。实验室和现场实验都证明了所提出的方法可以快速、准确地检测各种类型的水污染,并且是基于摄像头传感器的水环境监测的更好选择,优于其他方法。