Show simple item record

dc.contributor.authorMerz, Nicolasen
dc.date.accessioned2022-05-13T11:30:13Z
dc.date.available2022-05-13T11:30:13Z
dc.identifier.urihttp://hdl.handle.net/1974/30125
dc.description.abstractThis thesis proposes a novel method for instance segmentation in the Robotic Bin-Picking problem using a blend of deep learning and classical methods, titled EdgeNet. Pose estimation is used on sensed data to relay information for usage in object grasping. Industrial bins tend to be incredibly dense, prompting the interest in segmentation with the intent of further usage in pose estimation. Using an encoder- decoder network architecture for estimation of foreground, contours and centroids, followed by a post-processing algorithm, we have achieved performance that is com- petitive with Mask R-CNN while both training and evaluating faster. Furthermore, we have created a benchmark for a new task on the Fraunhofer bin-picking dataset and opened up further research into exploiting instance segmentation to isolate objects prior to pose estimation.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectcomputer visionen
dc.subjectinstance segmentationen
dc.subjectbin-pickingen
dc.subjectindustrial automationen
dc.subjectdeep learningen
dc.titleEdgeNet: Dense Instance Segmentation via Contour-Detection and Foreground Clusteringen
dc.typethesisen
dc.description.degreeM.A.Sc.en
dc.contributor.supervisorGreenspan, Michael
dc.contributor.departmentElectrical and Computer Engineeringen
dc.embargo.termsThe sponsor of the project is considering patenting the technique, and therefore thesis restriction would provide them more time to investigate and prepare for pursuing a patent.en
dc.embargo.liftdate2027-05-12T16:01:33Z
dc.degree.grantorQueen's University at Kingstonen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record