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  • Webinars
  • ITS Tech Talk: How Does Technology Support Effective Road Maintenance?

ITS Tech Talk: How Does Technology Support Effective Road Maintenance?


Road assessment has always been a very manual, tedious process to undertake. The process of driving the road network with a tablet or even worse a clipboard with a template on a sheet of paper is an outdated approach that must be improved. With the rapid improvement of technology, machine learning and AI can automate road assessment and enable predictive maintenance rather than reactive.

What you will learn

  • How computer vision can assist road assessment.
  • Moving from reactive to predictive road maintenance.
  • Automating the road assessment process.

Who should attend

  • Asset Managers
  • Council Members
  • Road Maintenance Contractors


  • Free for ITS Australia members
  • $20/session for non-members


Michael Cybulski CEO / Retina Visions


Retina Visions

Your questions answered by our presenter

Get in touch with Retina Visions.

Can we see a sample / demo of outputs of Retina system?

Can we get some clarification on the Dashcam used. Can it be mounted on any vehicle? How does it compensate for different lighting etc?

Does the system calculate the geographic location of street signs, road widths etc, and can that be exported to GIS?

What is the pricing model? Monthly subscription? Pay per km? Etc

Does RV have an online sample website or dataset that we can look at / try? Is this technology commercially available or is this a service offered directly by Retina Visions?

Is there a specific platform used to automate Work Order creation or is this technology agnostic?

Given the vision requires the roads to be driven, is the order of magnitude of that task similar to that of Google driving roads for their Street View experience?

One well-known limitation of computer vision solution is the system performance is highly affected by lighting and weather conditions (e.g., raining, wet road). Could you share your experience in dealing with this issue, and if you have paired cameras with other technologies such as lidar?

What were the hardest aspects of creating the machine learning model?

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