ArticleScientific data2026
A Public Image Dataset for Surface Defect Detection of Water-Based Coated Wood Products.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A Public Image Dataset for Surface Defect Detection of Water-Based Coated Wood Products.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed and released an image dataset for surface defect detection on waterborne painted wood products. The dataset comprises 13400 high-resolution images capturing four defect types: scratches, cracks, bubbles, and holes. These include 3645 bubble defects, 3498 scratch defects, 3256 crack defects, and 3001 hole defects. Data were acquired in an operational production facility in Jiangshan, China, using specialized industrial cameras. Professional annotators performed rigorous labeling to ensure accuracy. This dataset provides critical data support for deploying deep learning models in real-world industrial assembly lines. Researchers may leverage this dataset to develop automated machine learning solutions for multi-class defect detection. Such techniques enable timely defect detection and remediation, ensuring finish integrity and final product quality.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.