Intelligent automation for road inspection
Regional road authorities spend billions of euros a year on the maintenance of roads, traffic signs, street lights and bridges. The inspection is a time-consuming task requiring a great amount of human effort. Delays lead to more significant problems such as costly repairs, major traffic delays and can even cause accidents. The earlier issues are detected and more frequently they are checked, the better they can be addressed, reducing emergency repairs that shut down roads and cause traffic jams. By automating road inspection authorities will save time and money and improve public safety.
- Accurate prediction of road conditions at an earlier stage
- Improving quality inspection consistency and results
- Quickly reacting to road condition changes
- Faster evaluation of maintenance costs
- Increasing safety for both Road Inspectors, Road Maintenance Workers and the traffic surrounding them
- Road Inspectors curate examples
- BrainMatter views road damage and other road items
- Road Inspectors review new data
- BrainMatter learns to interpret road damage
- BrainMatter interprets anomalies
- BrainMatter is retrained on changes
- Road Inspectors define the rules
- BrainMatter initiates actions
- Evaluate actions and extend
- Acquire data and distribute captured data for annotations
- Video and image analysis undertaken by a domain expert, leveraging model assisted labeling
- Leverage AI trained models for an instant reality check against norm. Augment work orders for better insights and immediate action
- Implement your AI powered app in the field
- Expand and scale your digital intelligence throughout the organisation and ecosystem
- Continuous improvement and a feedback loop
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The intelligent automation platform for visual asset monitoring and inspection.
Asset-intensive industries require an integrated physical and digital view of assets, equipment, buildings and processes. BrainMatter provides a platform for intelligent automation of tasks that follows a perceive - decide - respond pattern.
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