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一个基于人工智能的智能手机系统用于石棉识别

Michael Rolfe¹, Samantha Hayes², Meaghan Smith¹

¹Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia.

Journal of hazardous materials

|November 2, 2023

概括

这项研究开发了一个基于智能手机的图像识别系统,用于石棉识别. 该系统实现了90%的准确性,为检测石棉材料提供了便携式和具有成本效益的解决方案.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
计算机科学计算机科学

背景情况:

由于环境和经济原因,对石棉的鉴定至关重要.
目前的方法依赖于光显微镜和专门安装的实验室分析.
需要更容易获得和便携式的石棉检测方法.

研究的目的:

开发基于智能手机的图像识别系统,用于石棉识别.
评估便携式显微镜与深度学习相结合的有效性.
为了比较不同卷积神经网络 (CNN) 模型的性能.

主要方法:

使用一个便携式30x显微镜与智能手机相机.
训练了一个深度学习模型,使用来自1000多个石棉水泥板样本的7328张图像.
测试并比较了三种CNN模型:ResNet101,InceptionV3和VGG_16. 这三种模型.

主要成果:

ResNet101实现了最高准确率 (98.46%) 的3.8%的损失.
基于电话的系统在没有专门的安装的情况下,90%的时间正确识别了石棉的独特性.
与其他经过测试的深度学习模型相比,ResNet101表现出更高的性能.

结论:

一个基于智能手机的便携式系统可以有效地识别石棉.
深度学习,特别是ResNet101,为准确检测石棉提供了一个有希望的方法.
这项技术为传统实验室方法提供了更容易获得和潜在的成本效益更高的替代方案.

关键词:

石棉石棉是一种石棉.宪法中立网络宪法中立网络危险材料的识别危险材料的识别图像识别图像识别

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