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相关实验视频

Updated: Jun 23, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

截碎废物流的图像捕获,细分和数据分析.

Heimo Gursch¹, Elke Schlager¹, Franz Thaler²

¹Know-Center GmbH, Knowledge Discovery, Graz, Austria.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA

|June 24, 2024

概括

数字化垃圾分类厂使用人工智能驱动的图像识别来分析碎碎的废物. 该系统通过识别废弃物成分和数量来提高回收效率,优化分类过程.

关键词:

在3D成像中使用3D成像.废弃物组合检测检测器废弃物组合检测器机器学习是机器学习.多光谱成像技术

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Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

相关实验视频

Last Updated: Jun 23, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

科学领域:

废物管理废物管理
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

回收行业面临着动态废物成分和不断增加的回收率的挑战.
数字化为优化和自动化垃圾分类流程提供了解决方案.

研究的目的:

开发和评估一个用于图像捕获,废弃物识别和碎碎废物流数据分析的系统.
为了使排序过程在数字化中的自动化适应和优化,废弃物分类厂.

主要方法:

在输送带上采集废物流的多谱二维和三维图像.
使用在合成数据上训练的组合卷积神经网络模型进行语义细分.
将细分结果与3D体积估计和切碎机械数据集成.

主要成果:

在语义细分性能方面,实现了高达75%的与欧盟相交.
开发了一个统一的数据表示,结合图像识别和体积估计.
能够根据集成数据集对加工废弃物质量的估计.

结论:

开发的系统展示了通过人工智能驱动的分析来提高垃圾分类效率的潜力.
在训练中使用合成数据可以减少手工标签工作,同时保持性能.
综合方法为在自动分类中准确估计加工废物质量提供了基础.