Reference number : GC-2017-Q2-00463, amendment number : , agreement type : G, recipient legal name : Wang, Yang (University of Manitoba)|Wang, Yang (Université du Manitoba), recipient country : CA, recipient province : nan, recipient city : Manitoba, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Sightline Innovation is a company that provides artificial intelligent products and solutions for businesses_x000D_
around the world. One area of interest for the company is visual inspection for quality control in_x000D_
manufacturing. The goal is to detect defects in manufactured parts and classify the type of defects. Since visual_x000D_
inspection by humans is time-consuming and costly, it is imperative to develop visual inspection systems that_x000D_
can automatically detect and classify defects from images. Traditional visual inspection systems are designed_x000D_
using hand-engineering features. However, since Sightline has clients from multiple industries, the visual_x000D_
inspection tasks of these clients can be vastly different depending on the industry. It is nearly impossible to_x000D_
design visual features that can work effectively across various visual inspection tasks. So engineers at Sightline_x000D_
end up spending a lot of time hand-tuning the system for each task._x000D_
Currently, the company has access to a large amount of images for various visual inspection tasks from_x000D_
different clients. The company is interested in exploiting these data to develop machine learning based_x000D_
solutions for visual inspection systems that can be quickly adapted to specific clients._x000D_
In the proposed research, we will study two specific problems. First, we will develop algorithms for localizing_x000D_
defects in images from weakly labeled data. Second, we will develop domain adaptation techniques for visual_x000D_
inspection. This project will produce a set of algorithms and prototypes for visual inspection that the company_x000D_
can build upon and integrate with its products., program purpose french : Sightline Innovation is a company that provides artificial intelligent products and solutions for businesses_x000D_
around the world. One area of interest for the company is visual inspection for quality control in_x000D_
manufacturing. The goal is to detect defects in manufactured parts and classify the type of defects. Since visual_x000D_
inspection by humans is time-consuming and costly, it is imperative to develop visual inspection systems that_x000D_
can automatically detect and classify defects from images. Traditional visual inspection systems are designed_x000D_
using hand-engineering features. However, since Sightline has clients from multiple industries, the visual_x000D_
inspection tasks of these clients can be vastly different depending on the industry. It is nearly impossible to_x000D_
design visual features that can work effectively across various visual inspection tasks. So engineers at Sightline_x000D_
end up spending a lot of time hand-tuning the system for each task._x000D_
Currently, the company has access to a large amount of images for various visual inspection tasks from_x000D_
different clients. The company is interested in exploiting these data to develop machine learning based_x000D_
solutions for visual inspection systems that can be quickly adapted to specific clients._x000D_
In the proposed research, we will study two specific problems. First, we will develop algorithms for localizing_x000D_
defects in images from weakly labeled data. Second, we will develop domain adaptation techniques for visual_x000D_
inspection. This project will produce a set of algorithms and prototypes for visual inspection that the company_x000D_
can build upon and integrate with its products., agreement title english : Machine learning for visual inspection systems, agreement title french : Machine learning for visual inspection systems, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-08-23, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada