construction
Image Annotation
for Pavement
Crack Detection
Germany
Overview
Overview
A leading German construction company needed reliable training data to develop an automated pavement crack detection model. Label Your Data provided consistent image annotations for 10,000 pavement frames, covering 22 types of road damage and challenging conditions.
annotated
categories
annotators
Client
A leading Germany-based construction company specializing in complex infrastructure projects across transportation, energy, tunneling, and civil engineering, sought an annotation partner to support the development of a computer vision model for automated pavement damage detection.
Challenges
The client needed consistent annotations across pavement images with varying damage severity and visibility conditions.
Some frames contained over 100 cracks, requiring annotators to distinguish closely spaced defects and accurately outline damaged areas. Rain and snow further complicated the task by obscuring pavement details.
Dense pavement damage, with over 100 cracks in some images
Precise defect boundaries on heavily damaged road surfaces
Reduced visibility caused by rain and snow
Solution
Label Your Data assigned four dedicated annotators trained on the client's project-specific guidelines. Using CVAT, the team annotated pavement defects with polygons across 22 damage categories and applied 10 additional tags to describe road conditions.
Vehicles were also identified with bounding boxes to support anonymization.
By following a standardized annotation specification, the team maintained consistent labeling across complex pavement imagery, providing structured training data for the client’s damage detection model.
Building a Computer Vision Model
for Infrastructure Inspection?
Results
Label Your Data delivered 10,000 consistently annotated pavement images to support the client's ongoing development of an automated damage detection model.
While final performance metrics were not yet available, the collaboration showed encouraging outcomes:
Expanded training data covering 22 pavement damage categories and diverse road conditions.
Positive indications of improved accuracy in detecting and classifying pavement damage.
Reduced manual frame review time, helping streamline pavement analysis workflows.
Why AI Teams Choose Label Your Data
Data Annotation for Complex Environments
Rely on consistent, high-quality output for complex datasets, detailed taxonomies, and edge cases.
Structured Quality from Pilot to Production
Get quality engineered into every step through onboarding, evolving guidelines, QA, and continuous feedback.
Flexible and Scalable Operations
Adjust team capacity, project size, and delivery model as you scale, with no setup fees or long-term lock-ins.
An Integrated Delivery Partner
Align on goals, workflows, and expectations with a team that integrates into your process from day one.
Projects Led by Annotation Experts
Work with former annotators who understand annotation complexity, quality standards, and high-volume delivery.
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After running pilots with several annotation providers, Label Your Data delivered the strongest results by a clear margin, standing out on turnaround time, annotation quality, and the responsiveness of their feedback loops.
Maxime Debarbat
Senior ML Engineer (GenAI)
Trusted by ML Professionals
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