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RT-CLAD:在饮用水处理厂内基于人工智能实时检测虫幼虫

Goeun Jang¹, Wooseong Yeo², Meeyoung Park²

¹Department of Environmental and Energy Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea.

Sensors (Basel, Switzerland)

|January 11, 2024

概括

一个新的实时虫幼虫检测系统 (RT-CLAD) 使用深度学习来准确识别自来水中的幼虫. 这项技术通过解决水处理厂面临的挑战,提高了供水安全.

科学领域:

环境科学环境科学
水质监测水质监测
人工智能的人工智能

背景情况:

在南韩,自来水中的虫幼虫引起了公众的关注.
防止供水系统中的幼虫污染仍然是一个重大挑战.

研究的目的:

为饮用水处理厂开发和实施一个实时虫幼虫检测系统 (RT-CLAD).
通过先进的检测方法提高供水安全.

主要方法:

使用深度学习算法:卷积神经网络 (CNN),你只看一次 (YOLO) 和残余神经网络 (ResNet).
采用多光谱摄像机在广泛的光谱范围内获取图像,捕捉独特的波纹波段.
预处理的波形转换图像,以最大限度地减少数据大小,以实现高效的处理.

主要成果:

这三种深度学习算法 (CNN,YOLO,ResNet) 在检测虫幼虫时都实现了100%的准确性.
与YOLO和ResNet.Net相比,CNN算法由于更快的处理速度和更简单的架构,显示出更高的效率.

结论:

开发的RT-CLAD系统有效地解决了水处理中的幼虫检测挑战.
这种人工智能驱动的系统预计将大大提高供水的安全性和可靠性.

关键词:

奇罗诺米德幼虫的幼虫深度学习是一种深度学习.饮用水处理厂饮用水处理厂幼虫检测系统幼虫检测系统多光谱图像图像多光谱图像

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