Evidence map›Paper›PMID 42733551›Full record

ArticleData in brief2026

DefectAtlas-20: A real-world image dataset for joint material and surface-condition classification.

Ricardo Buettner, Leopold Fischer-Brandies, Lukas Pasold, Luca Eisentraut

Abstract read
In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ricardo BuettnerChair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Holstenhofweg 85, Hamburg, 22043, Germany.
Leopold Fischer-BrandiesChair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Holstenhofweg 85, Hamburg, 22043, Germany.
Lukas PasoldChair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Holstenhofweg 85, Hamburg, 22043, Germany.
Luca EisentrautChair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Holstenhofweg 85, Hamburg, 22043, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reliable automated surface inspection requires image datasets that reflect the conditions under which material damage occurs in practice. However, many existing defect datasets focus on individual materials, specific components, or controlled imaging environments, limiting their suitability for studying material-dependent surface defect appearance and their performance robustness under real-world variation. This article presents DefectAtlas-20, a dataset of 13,233 RGB photographs organized into 20 classes formed by combining ten commonly encountered material categories with defect and no-defect surface conditions. The materials comprise uncoated and coated metal, uncoated and coated wood, rubber, textile, cardboard, glass, cable, and hard plastic. Images were acquired using consumer mobile devices in heterogeneous indoor and outdoor field environments around Hamburg, Germany. They capture variation in illumination, viewing angle, background, object geometry, surface texture, reflectance, and usage condition. The defect classes contain naturally occurring damage, while the corresponding no-defect classes show visually comparable surfaces without visible defects. All images were collected according to a predefined protocol, manually labeled, independently reviewed, centrally quality-controlled, anonymized through metadata removal, and converted to lossless PNG format in the sRGB color space. The released data package includes 512 × 512-pixel images, class and acquisition metadata, and documentation of the collection and curation process. The dataset provides a benchmark for 20-class material-condition classification and additionally supports binary defect recognition, material classification, material-specific defect analysis, transfer learning, and domain-generalization research. Its material diversity and real-world acquisition conditions facilitate the development and evaluation of computer-vision methods for surface inspection, maintenance, refurbishment, and damage assessment.

Indexed as

Computer visionImage datasetIndustrial inspectionMaterial damageReal-world environmentsScratch detectionSurface defectsVisual inspection

Identifiers

PMID42733551
PMCPMC13571519

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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.