Mobiusi

Bridge Surface Damage Image Segmentation Dataset

#image segmentation #damage detection #computer vision #deep learning #bridge maintenance #building structure inspection #surface damage detection #building safety assessment
  • 500 records
  • 1.5G
  • JPG
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-02-04

AI Analysis & Value Prop

The current construction industry often faces challenges of inefficiency and lack of accuracy in damage detection during bridge maintenance and safety assessments. Existing solutions mostly rely on manual inspection, which is time-consuming and highly subjective. This dataset aims to accurately detect damage on bridge surfaces using image segmentation technology, thereby improving inspection efficiency and accuracy. The data is collected in real bridge environments using high-definition photographic equipment, covering multiple weather conditions and time periods. Quality control starts with multiple rounds of annotation and expert reviews to ensure annotation accuracy and consistency. The annotation team consists of professionals with a background in civil engineering, with a total size of 50 people. Data preprocessing includes denoising, enhancement, and color correction, stored as JPG files in a layered structure for quick retrieval and processing.

Dataset Insights

Sample Examples

c18a7691**.jpg|600*771|123.73 KB

Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
damage_typestringDescribes the type of damage on the bridge surface, such as cracks, spalling, corrosion, etc.
damage_severitystringEvaluates the severity of damage on the bridge surface, such as minor, moderate, or severe.
damage_areastringIdentifies the specific area on the bridge surface that is damaged.
weather_conditionsstringWeather conditions when the image was taken, such as sunny, rainy, cloudy, etc.
lighting_conditionsstringLighting conditions during image capture, such as well-lit, shadowed, etc.
image_qualitystringAssessment of the image clarity and accuracy, such as high definition, blurry, etc.
camera_anglestringThe angle of the camera when capturing the image, such as frontal, top-down, side view, etc.

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What type of construction projects can this dataset be used for?
This dataset can be used for bridge surface damage detection, which is an important tool in bridge maintenance and management.
What common types of bridge surface damages can be identified using this dataset?
The dataset can identify common bridge surface damages such as cracks, spalling, and corrosion.
What techniques can be used for image segmentation with this dataset?
Deep learning techniques such as Convolutional Neural Networks (CNN) and U-Net models can be used for image segmentation.
Is this dataset intended for industry or academia?
This dataset can be used in industry for bridge monitoring projects as well as in academia for research and experimentation.
In what ways does this dataset help improve bridge maintenance?
The dataset helps improve the efficiency and accuracy of bridge damage detection, thereby improving maintenance strategies.

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Cite this Work

@dataset{Mobiusi2026,
  title={Bridge Surface Damage Image Segmentation Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/74eabd55678204707b758bfe70b92eca?dataset_scene_id=18},
  urldate={2026-02-04},
  keywords={bridge damage detection dataset, image segmentation dataset, building damage assessment, bridge safety inspection},
  version={1.0}
}

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