| dc.contributor.advisor | Wörgötter, Florentin Prof. Dr. | |
| dc.contributor.author | Yildiz, Erenus | |
| dc.date.accessioned | 2021-04-30T11:08:19Z | |
| dc.date.available | 2021-05-07T00:50:14Z | |
| dc.date.issued | 2021-04-30 | |
| dc.identifier.uri | http://hdl.handle.net/21.11130/00-1735-0000-0008-5811-2 | |
| dc.identifier.uri | http://dx.doi.org/10.53846/goediss-8573 | |
| dc.language.iso | eng | de |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject.ddc | 510 | de |
| dc.title | An Intelligent Visual Analysis Scheme for Automatic Disassembly Processes in the Recycling Industry | de |
| dc.type | doctoralThesis | de |
| dc.contributor.referee | Grabowski, Jens Prof. Dr. | |
| dc.date.examination | 2021-04-20 | |
| dc.description.abstracteng | This thesis aims to develop and deploy a visually intelligent disassembly
scheme to automate the recycling routines for end-of-life products. The
recent developments in artificial intelligence and computer vision are yet
to be utilized in the E-Waste recycling industry, a shortcoming this thesis
addresses. We ask to what extent and in what ways state-of-the-art deep
learning methods could constitute an intelligent and generalizing scheme
that can be used to disassemble commonly found computer parts such
as hard drives and graphical processing units, that are known to contain
valuable metals.
Using relevant metrics to evaluate the accuracy and performance of
individual components and the entire system altogether, we empirically
show that methods based on deep learning and computer vision are well
suited for estimating the state of disassembly and inferring the required
visual parameters for possible action executions.
The significance of this study is that it introduces an industry-oriented
scheme that only requires off-the-shelf sensors to operate, and can be
repurposed to work with new products. The work has also been part of
Horizon 2020 project "IMAGINE", aimed to develop a fully automated
disassembly robot to be used in recycling plants. Therefore, the results
obtained and presented in this thesis have also been used in the IMAGINE
project. | de |
| dc.contributor.coReferee | Yahyapour, Ramin Prof. Dr. | |
| dc.contributor.thirdReferee | Damm, Carsten Prof. Dr. | |
| dc.contributor.thirdReferee | Dellen, Babette Prof. Dr. | |
| dc.contributor.thirdReferee | Ecker, Alexander Prof. Dr. | |
| dc.subject.eng | Deep learning | de |
| dc.subject.eng | object detection | de |
| dc.subject.eng | object classification | de |
| dc.subject.eng | image segmentation | de |
| dc.subject.eng | computer vision | de |
| dc.identifier.urn | urn:nbn:de:gbv:7-21.11130/00-1735-0000-0008-5811-2-7 | |
| dc.affiliation.institute | Fakultät für Mathematik und Informatik | de |
| dc.subject.gokfull | Informatik (PPN619939052) | de |
| dc.description.embargoed | 2021-05-07 | |
| dc.identifier.ppn | 1756853010 | |