Roadscan AI: Computer Vision for Road Condition Assessment
The Challenge
Road-condition assessment can require significant field effort to identify visible surface damage and infrastructure issues.
Traditional inspection workflows may involve manual observation, documentation, and follow-up assessment. Computer vision creates an opportunity to analyse road footage and identify potential issues that can then be reviewed by inspection teams.
The challenge is to convert mobile video into useful visual information while accounting for real-world conditions such as:
- Camera movement
- Different viewing angles
- Lighting changes
- Road-surface variation
- Motion blur
- Weather and environmental conditions
- Different road and infrastructure types
The Roadscan AI Concept
Roadscan AI is a Rootblock Labs product initiative exploring the use of computer vision for road-condition assessment.
The concept is based on capturing road footage using an iPhone or another supported recording device and processing that footage through a computer vision pipeline.
The system is being explored for potential detection of visible road and infrastructure issues.
Planned Detection Areas
The product initiative is exploring computer vision approaches for identifying potential issues such as:
- Road cracks
- Surface damage
- Missing manhole covers
- Other visible road hazards
Detections would be intended for review rather than being treated as automatically verified infrastructure defects.
Development Workflow
The development approach involves several stages.
- Data Collection
- Collect representative road footage covering different road conditions, camera positions, lighting environments, and environmental conditions.
- Data Annotation
- Create labelled datasets containing relevant road-surface and infrastructure conditions.
- Model Development
- Evaluate suitable computer vision and object-detection approaches for the target detection tasks.
- Model Evaluation
- Test the models against representative footage and annotated ground truth.
- Video Processing
- Develop a pipeline capable of processing recorded or supported video input and extracting potential detections.
- Review Workflow
- Organise detections so that potential road issues can be reviewed and investigated by the relevant team.
Potential Applications
If validated, the concept could support workflows such as:
- Road maintenance planning
- Infrastructure inspection
- Municipal asset management
- Road-condition monitoring
- Prioritisation of locations requiring further inspection
Current Status
Roadscan AI is a product in development.
Detection accuracy, coverage, reliability, and operational benefits have not yet been presented as validated production results. Future performance claims should be based on documented testing against representative datasets and defined evaluation criteria.
Why This Matters
The project demonstrates how computer vision can be applied to a practical infrastructure problem while highlighting the engineering work required to move from an AI concept to a validated real-world system.
The objective is not simply to detect objects in video, but to develop a workflow in which computer vision outputs can become useful information for infrastructure inspection.
Technologies
Rootblock Labs Capability
Rootblock Labs works across computer vision, AI model development, video processing, edge deployment, and real-world application engineering.
