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Machine learning for visual inspection systems

视觉检测系统的机器学习

基本信息

批准号：
514591-2017
负责人：
Wang, Yang
金额：
$ 1.82万
依托单位：
University of Manitoba
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
起止时间：
2017-01-01 至 2018-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640692
关键词：
Machine learning visual inspection systems

项目摘要

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.

Sightline Innovation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制造业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。由于人工视觉检测耗时且成本高，因此必须开发能够自动检测和分类图像中缺陷的视觉检测系统。传统的视觉检测系统是使用手工工程设计的。然而，由于Sightline拥有来自多个行业的客户，这些客户的视觉检测任务可能会因行业而异。这几乎是不可能设计的视觉功能，可以有效地工作在各种视觉检测任务。因此，Sightline的工程师们最终会花费大量时间为每项任务手动调整系统。目前，该公司可以访问来自不同客户的大量图像，用于各种视觉检测任务。该公司有兴趣利用这些数据来开发基于机器学习的视觉检测系统解决方案，这些解决方案可以快速适应特定的客户。在拟议的研究中，我们将研究两个具体问题。首先，我们将开发算法，用于从弱标记数据中定位图像中的缺陷。其次，我们将开发视觉检测的域适应技术。该项目将产生一套算法和原型的视觉检测，该公司可以建立在其产品和集成。