Road Defect Damage and Crack Detection Image Dataset
#image classification #object detection #image segmentation #road detection #construction maintenance #urban planning
- 500 records
- 1.2G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
The current construction and real estate industry faces challenges in road damage detection, especially in the maintenance and management of urban infrastructure. Existing solutions often rely on manual inspections, which are time-consuming and error-prone, while traditional monitoring equipment has limitations in defect identification accuracy and detection efficiency. This dataset aims to provide high-quality image data for the automated detection of road defects, meeting the needs of intelligent monitoring and maintenance. Data collection is carried out using high-definition camera equipment in diverse road environments, including urban roads and highways. Quality control is strict, including multiple rounds of annotation and consistency checks, and the annotation team consists of experts and technicians with over five years of industry experience. Data preprocessing steps include image enhancement, size normalization, and format conversion, with final storage in JPG format, organized by geographic location and road surface type.
Dataset Insights
Sample Examples
62f00979**.jpg|462*257|31.76 KB
1ec1d281**.jpg|462*257|14.01 KB
a0865142**.jpg|390*259|13.54 KB
12efe24f**.jpg|345*259|17.24 KB
535b3551**.jpg|260*259|7.47 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| defect_type | string | The specific type of road defect detected in the image, such as cracks or potholes. |
| defect_severity | string | The severity level of the defect detected in the image, which can be classified as minor, medium, or severe. |
| defect_area | float | The area of the defect in the image, expressed in square units. |
| defect_location | string | The specific location of the defect in the image, usually expressed with coordinates. |
| surface_type | string | The type of surface of the road in the image, such as asphalt or concrete. |
| light_conditions | string | The lighting conditions when the image was taken, such as sunny, cloudy, or nighttime. |
| weather_conditions | string | The weather conditions during the image capture, such as sunny, rainy, or snowy. |
| vehicle_presence | boolean | Whether there is a vehicle present in the image. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| 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 |
Frequently Asked Questions
- What fields is this dataset mainly applied to?
- This dataset is mainly applied in the construction and real estate industry, particularly in road maintenance and detection.
- How were the images in this dataset acquired?
- The images in this dataset were acquired using high-definition camera equipment specifically for road damage detection.
- Why is this dataset important for the construction industry?
- This dataset helps in identifying and assessing road damages, facilitating timely repairs and ensuring road safety, making it very important for the construction industry.
- How can this dataset be used for road damage detection?
- Users can utilize the images in this dataset for machine learning model training to enhance the accuracy of automated road damage detection.
- Is this dataset suitable for developing automated damage detection systems?
- Yes, this dataset is very suitable for developing automated road damage detection systems as it provides high-quality image data.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Road Defect Damage and Crack Detection Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/d31d28c51a35042231e106763a87a28d},
urldate={2026-02-04},
keywords={road defect detection, construction maintenance, image dataset},
version={1.0}
}Using this in research? Please cite us.
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