Data supporting: 'Autonomous Ground Refuelling Approach for Civil Aircrafts using Computer Vision and Robotics'

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dc.contributor.authorYildirim, Suleyman
dc.date.accessioned2024-06-03T06:45:47Z
dc.date.available2024-06-03T06:45:47Z
dc.date.issued2022-08-15 09:05
dc.description.abstractAircraft Refuelling Adaptor Localisation - v3 2022-03-15 12:35pm It includes 881 images. Letters are annotated in COCO format. The following pre-processing was applied to each image: The following augmentation was applied to create 3 versions of each source image: * 50% probability of horizontal flip * 50% probability of vertical flip * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down * Random Gaussian blur of between 0 and 1 pixels
dc.identifier.citationYildirim, Suleyman (2022). Data supporting: 'Autonomous Ground Refuelling Approach for Civil Aircrafts using Computer Vision and Robotics'. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.20445579
dc.identifier.doi10.17862/cranfield.rd.20445579
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21767
dc.publisherCranfield University
dc.relation.supplementshttps://doi.org/10.1109/DASC52595.2021.9594312'
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectaircraft refuelling adaptor'
dc.subject'pressurised refuelling adaptor'
dc.titleData supporting: 'Autonomous Ground Refuelling Approach for Civil Aircrafts using Computer Vision and Robotics'
dc.typeDataset

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