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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Coating Defect Detection Dataset

Authors: Pérez García de la Puente, Natalia Lourdes; Mateos Luengo, Javier; Lario Femenia, Joan; López López, Eric; Aksu, Salih; Colomer, Adrián; Naranjo, Valery;

Coating Defect Detection Dataset

Abstract

This dataset includes 2697 RGB images of Wind Tower Painting parts, along with bounding box annotations (YOLO format) for defect localization and classification. In 'class_labels.txt' a legend of the three labels of the defect classes is given. In 'metadata.csv' each image filename is related to the acquisition or section setup code. It also indicates the main label (in some images there is more than one defect), if a cold or warm white balance and high or low surface contrast are captured.

Related Organizations
Keywords

NDT, defect detection, Wind Towers, Painting process, AI-enhanced, vision inspection, product quality

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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