Drone Footage Wind Turbine Surface Damage Detection
dc.contributor.author | Foster, A | |
dc.contributor.author | Best, O | |
dc.contributor.author | Gianni, Mario | |
dc.contributor.author | Khan, Asiya | |
dc.contributor.author | Collins, KM | |
dc.contributor.author | sharma, sanjay | |
dc.date.accessioned | 2022-11-07T11:36:35Z | |
dc.date.available | 2022-11-07T11:36:35Z | |
dc.date.issued | 2022-06-26 | |
dc.identifier.isbn | 9781665478229 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/19864 | |
dc.description.abstract |
In this work a new publicly available dataset of wind turbine surface damage images is presented. Moreover, a comparison between ResNet-101 Faster R-CNN and YOLOv5 for Wind Turbine Surface Damage Detection is analysed and performance of these models on drone footage with active turbines is also discussed. Results show that YOLOv5 outperforms ResNet-101 Faster R-CNN in predicting the bounding box coordinates of the damaged surfaces of the wind turbines. However, unlike YOLOv5, ResNet-101 Faster R-CNN estimates an entire instance of damage as a single prediction. | |
dc.format.extent | 1-5 | |
dc.language.iso | en | |
dc.publisher | IEEE | |
dc.subject | 7 Affordable and Clean Energy | |
dc.title | Drone Footage Wind Turbine Surface Damage Detection | |
dc.type | conference | |
dc.type | Conference Proceeding | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000853856800014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.date-start | 2022-06-26 | |
plymouth.date-finish | 2022-06-29 | |
plymouth.volume | 00 | |
plymouth.conference-name | 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) | |
plymouth.publication-status | Published | |
plymouth.journal | 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) | |
dc.identifier.doi | 10.1109/ivmsp54334.2022.9816220 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering | |
plymouth.organisational-group | /Plymouth/Research Groups | |
plymouth.organisational-group | /Plymouth/Research Groups/Marine Institute | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.rights.embargoperiod | Not known | |
rioxxterms.funder | Engineering and Physical Sciences Research Council | |
rioxxterms.identifier.project | Supergen ORE hub 2018 | |
rioxxterms.versionofrecord | 10.1109/ivmsp54334.2022.9816220 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | |
plymouth.funder | Supergen ORE hub 2018::Engineering and Physical Sciences Research Council |