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Data · dataset · 2024

YOLO-Seg Model with Tiling Movement for Detecting Paddy Parcels using High-Resolution Drone Image

Listed in Teesside University Research Data Repository

This study aims to develop a system that combines object detection and segmentation capabilities in high-resolution images using the YOLO-Seg model for paddy parcel monitoring based on drone imagery.

Description

For the first time, we propose a novel tiling movement technique and systematically analyze optimal overlap rates to reduce tile boundary errors that occur in large-scale drone image processing.

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Where it is published

Catalogue records · 1

Topics

Inferred from text
Computer vision 76% · Image 75%
Provenance · 1 source records, 13 field assertions
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Teesside University Research Data Repositoryoai:data.mendeley.com/sgx3mnxj5h.111 d agoJSON v1
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concepts[field].anzsrc:field:460304enrichment · researchdata tees ac uktaxonomy-embedding@1.0.0title+keywords+description (76%)
concepts[field].local:field:computer-science-aimapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[field].local:field:earth-environmentalmapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
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concepts[field].local:field:social-sciencemapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[modality].local:modality:imageenrichment · researchdata tees ac ukkeyword-concept-rules@1.0.0title+description (75%)
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