Concrete Structure Disease Visual Diagnosis and Defect Annotation Dataset
- Records
- 14886
- Size
- 0.5GB
- Format
- piece
- Provider
MOBIUSI INC- Updated
- v1.02026-11-04
This dataset is designed for identifying surface damage in concrete structures. It includes image and annotation file paths, along with metadata such as category IDs, image dimensions, image formats, and unique image identifiers. The sample count, specific defect categories, and annotation guidelines are not specified in the available information; the data can support visual defect analysis, model development and evaluation, as well as research on structural inspection and maintenance.
Overview
Dataset Insights
Preview
Sample Examples
04365753**.jpg||0 B
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| image_id | string | A unique identifier used to distinguish each image record. |
| image_path | string | The path or location of the image file. |
| label_path | string | The path or location of the annotation file associated with the image. |
| category_id | array | The identifier for the category assigned to the image or its annotation. |
| image_width | number | The width of the image, typically measured in pixels. |
| image_format | string | The file format of the image. |
| image_height | number | The height of the image, typically measured in pixels. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use
Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization
No PII, no real company names, simulated scenarios follow industry standards
Compliance System
Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs
FAQ
Frequently Asked Questions
- What does the dataset contain?
- The available fields include unique image identifiers, image paths, annotation file paths, category IDs, image width and height, and image formats. These fields can be used to associate images with defect annotations.
- How many samples and defect categories are included?
- The available information does not specify the sample count, defect categories, or annotation guidelines. Check the dataset manifest and annotation documentation before use to confirm that the category definitions and sample distribution meet your requirements.
- Which model tasks is it suitable for?
- Potential tasks include concrete surface damage classification, defect object detection, and region segmentation. The tasks that can be trained depend on the annotation formats actually included in the annotation files.
- How can I obtain the data and verify licensing?
- Availability, file formats, and licensing terms should be confirmed on the provider's product page or in the applicable license agreement. Before commercial deployment or redistribution, verify the permitted uses, restrictions, and attribution requirements.
Can't find the data you need?
Post a request and let data providers reach out to you.
Get this Dataset
High-quality data, ready for your next AI breakthrough.
Cite this Work
@dataset{Mobiusi2026,
title={Concrete Structure Disease Visual Diagnosis and Defect Annotation Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/3573a267921dc8c62a71ebcf7b1de6c3?dataset_scene_id=18},
urldate={2026-11-03},
keywords={concrete disease dataset,concrete defect annotations,structural crack detection,concrete image dataset,infrastructure inspection,defect object detection,disease image classification,concrete visual diagnosis},
version={1.0}
}Using this in research? Please cite us.