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Machine learning for visual inspection systems

Machine learning for visual inspection systems
视觉检测系统的机器学习
批准号:
514591-2017
负责人:
Wang, Yang
金额:
$1.82万
依托单位:
依托单位国家:
加拿大
项目类别:
Engage Grants Program
财政年份:
2017
资助国家:
加拿大
项目状态:
已结题
起止时间:
2017-01-01 至 2018-12-31

项目摘要

项目成果

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中文摘要
翻译
视线创新是一家为全球企业提供人工智能产品和解决方案的公司。该公司感兴趣的一个领域是生产过程中质量控制的目视检查。目标是检测制造零件中的缺陷并对缺陷类型进行分类。由于人工目视检测既耗时又昂贵,因此开发能够从图像中自动检测和分类缺陷的目视检测系统势在必行。传统的视觉检测系统是利用手工工程的特点设计的。然而,由于Sightline有来自多个行业的客户,这些客户的视觉检查任务可能因行业而异。几乎不可能设计出能够在各种视觉检测任务中有效工作的视觉特征。因此,sightline的工程师们最终要花很多时间为每个任务手动调整系统。目前,公司可以访问来自不同客户的各种视觉检测任务的大量图像。该公司有兴趣利用这些数据为视觉检测系统开发基于机器学习的解决方案,这些解决方案可以快速适应特定客户。在拟进行的研究中,我们将研究两个具体问题。首先,我们将开发从弱标记数据中定位图像缺陷的算法。其次,我们将开发用于视觉检测的领域自适应技术。该项目将产生一套用于视觉检查的算法和原型,公司可以在此基础上构建并集成到其产品中。
英文摘要
Sightline Innovation is a company that provides artificial intelligent products and solutions for businessesaround the world. One area of interest for the company is visual inspection for quality control inmanufacturing. The goal is to detect defects in manufactured parts and classify the type of defects. Since visualinspection by humans is time-consuming and costly, it is imperative to develop visual inspection systems thatcan automatically detect and classify defects from images. Traditional visual inspection systems are designedusing hand-engineering features. However, since Sightline has clients from multiple industries, the visualinspection tasks of these clients can be vastly different depending on the industry. It is nearly impossible todesign visual features that can work effectively across various visual inspection tasks. So engineers at Sightlineend up spending a lot of time hand-tuning the system for each task.Currently, the company has access to a large amount of images for various visual inspection tasks fromdifferent clients. The company is interested in exploiting these data to develop machine learning basedsolutions for visual inspection systems that can be quickly adapted to specific clients.In the proposed research, we will study two specific problems. First, we will develop algorithms for localizingdefects in images from weakly labeled data. Second, we will develop domain adaptation techniques for visualinspection. This project will produce a set of algorithms and prototypes for visual inspection that the companycan build upon and integrate with its products.
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Visual Recognition Beyond Supervised Learning
  • 批准号:
    RGPIN-2019-05362
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.04万
  • 财政年份:
    2022
  • 负责人:
    Wang, Yang
  • 依托单位:
Visual Recognition Beyond Supervised Learning
  • 批准号:
    RGPIN-2019-05362
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.04万
  • 财政年份:
    2021
  • 负责人:
    Wang, Yang
  • 依托单位:
Visual Recognition Beyond Supervised Learning
  • 批准号:
    RGPIN-2019-05362
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.04万
  • 财政年份:
    2020
  • 负责人:
    Wang, Yang
  • 依托单位:
Visual Recognition Beyond Supervised Learning
  • 批准号:
    RGPIN-2019-05362
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.04万
  • 财政年份:
    2019
  • 负责人:
    Wang, Yang
  • 依托单位:
国内基金
海外基金
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  • 批准年份:
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