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Supporting data for Deep Learning and Machine Vision based approaches for automated wood defect detection and quality control.

Name: Supporting data for Deep Learning and Machine Vision based approaches for automated wood defect detection and quality control.
Keywords: Bounding box labels, Semantic map labels, Wood defects image database, Wood surface defect, Wood quality control

Research datakeyboard_double_arrow_right Dataset 15 Apr 2021Publisher:Zenodo

Authors: Kodytek Pavel; Bodzas Alexandra; Bilik Petr;

doi: 10.5281/zenodo.4694694 , 10.5281/zenodo.4694695

Supporting data for Deep Learning and Machine Vision based approaches for automated wood defect detection and quality control.

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Abstract

The dataset contains more than 43 000 labeled wood surface defects and covers overall ten types of the most common defects, including live knots, dead knots, knots with crack, cracks, resins, marrows, quartzity, missing knots, blue stain, and overgrown. Each image in the dataset is provided with a semantic map and a bounding box label that allows performing semantic segmentation as well as localization tasks. All data were collected directly from a wood production line during the manufacturing process.

Related Organizations

Technical University of Ostrava
Czech Republic

Keywords

Bounding box labels, Semantic map labels, Wood defects image database, Wood surface defect, Wood quality control

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	citations 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).	2
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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

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