Data · dataset · 2026
BeltCrack: the First Sequential-image Industrial Conveyor Belt Crack Detection Dataset
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Description
Dataset IntroductionThe BeltCrack Datasets are designed for industrial conveyor belt crack detection, consisting of two specialized sub-datasets: BeltCrack14ks (strong saliency, dense crack scenes) and BeltCrack9kd (weak saliency, sparse crack scenes), tailored to address diverse industrial inspection challenges.Data Collection & ProcessingData was captured in real-world industrial environments using portable smartphones mounted on adjustable tripods to minimize motion blur.
The cameras operated at 30 FPS with 16:9 aspect ratio resolutions (3840×2160 and 1920×1080). Capturing conditions were deliberately diversified to ensure robustness:• Multi-perspective views: top-down and bottom-up angles• Time-varying illumination: from strong morning light to low evening illumination• Multi-weather scenarios: sunny, rainy, and snowy conditions• Dynamic belt speeds: ranging from 2 to 6 m/sOriginal three-channel RGB video streams were split into frame sequences, followed by frame-by-frame pixel-level bounding box annotation, and finally structured into a hierarchical format compatible with the COCO specification.
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The dataset currently focuses on a single crack class, with inherent support for future multi-class expansion.Sub-datasets DetailsBeltCrack14ks: Contains 14,087 images across 29 sequences, split into training, validation, and test sets at a 6:2:2 ratio (8,773 training, 2,596 validation, 2,718 test images). It has an average of 485.76 images per sequence and 2.65 cracks per image, featuring strong crack saliency and dense crack distributions.
Cracks are categorized by pixel area into tiny (<100), small (100–1000), medium (1000–10000), large (10000–100000), and huge (≥100000), with a long-tailed normal area distribution.BeltCrack9kd: Comprises 9,645 images from 42 sequences, split into training and test sets at an 8:2 ratio (7,697 training, 1,948 test images). It has an average of 229.64 images per sequence and 1.34 cracks per image, characterized by weak crack saliency and sparse, discrete crack distributions.
It also follows the same crack area categorization and exhibits higher crack discreteness, posing greater challenges for sparse crack detection.
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- DOI doi.org/10.57760/sciencedb.31181 ↗
DOI / persistent id · from scidb cn
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- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
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Topics
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- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Geomatic engineering 70% · Image 75% · Video 75%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.31181 | 11 d ago | JSON v1 |
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| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4013 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (70%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
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| concepts[modality].local:modality:image | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:video | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |