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深度学习使得在现场提取和图像处理后,在消费品中快速和低成本检测出微塑料

Md Zayed Bin Zahir Arju¹, Nafisa Amin Hridi¹, Lamiya Dewan¹

¹Department of Electrical and Electronic Engineering, University of Dhaka Dhaka-1000 Bangladesh mainul.eee@du.ac.bd mahabib@du.ac.bd.

|April 7, 2025

概括

这项研究引入了一种低成本的手机显微镜,用于检测日常产品中的微塑料 (MP). 该方法达到98%的准确性,为MP监控提供了快速而实惠的解决方案.

科学领域:

环境科学环境科学
分析化学分析化学
材料科学材料科学材料科学

背景情况:

微塑料 (MP) 污染是一个日益严重的环境和健康问题.
目前的MP检测方法昂贵,耗时,劳动密集.
需要可访问和高效的MP检测技术.

研究的目的:

开发一个简单的,低成本的平台,使用手机显微镜检测微塑料.
从盐,糖和牙膏等常见消费产品中提取和图像MP.
利用深度学习来准确地识别图像中的MP.

主要方法:

从各种矩阵中提取MP,使用密度分离与ZnCl2和H2O2.
这些图像是用一台配备10美元显微镜附件 (TinyScope) 的手机拍摄的.
对于MP检测,YOLOv5深度学习模型进行了训练和验证,达到98%的准确性.

主要成果:

在盐,糖,茶袋,牙膏和牙粉中成功检测到微塑料.
YOLOv5模型在从捕获的图像中识别国会议员方面表现出高准确度.
ATR-FTIR和FE-SEM证实了塑料颗粒的存在和形态.

结论:

开发的基于手机的显微镜平台为MP检测提供了快速,准确和负担得起的方法.
这种方法适用于资源较少的环境,可以频繁监控消费品中的MP内容.
该研究强调了可访问技术在解决环境污染问题的潜力.

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