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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process of driving the road network with a tablet or even worse a clipboard with a template on a sheet of paper is an outdated approach that must be improved.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.
Michael Cybulski CEO / Retina Visions CEO / Retina Visions
Michael founded Retina Visions in 2016 and has since established a strong presence within the transport industry. Trained in civil engineering with a history in the road construction and transport industries, Michael has worked on underground cable projects, road surface condition assessments, business development of asset management systems and big data analytics. Michael has worked with both private industry and the public sector, providing enhancements to businesses to improve efficiencies in how they manage and utilise their data. Michael was awarded the ITS Australia Young Professional of the Year award in 2018.
Get in touch with Retina Visions.
Definitely, if you get in touch with us we can provide sample outputs.
The dash-cam is fitted inside the windscreen of any vehicle and attaches with a suction cup mount. It plugs into the cigarette outlet in the front console and takes about 5 minutes to install.
All output data includes coordinates and can be imported into GIS applications.
The service is charged on a monthly subscription, and all data that Retina Visions can capture is provided for the month. Outputs are given on a daily basis either through GeoJSON files sent to emails or an API that can be called by the client.
This service is offered by Retina Visions. We have a demo account on our portal for anyone to view. If you get in touch with us we can provide login details.
Retina Visions provides GeoJSON outputs that can be imported through an API into asset management systems. The data is NOT encrypted and very human readable, so it can be streamed into all asset management systems.
The dash-cam is provided to councils to fit into either their maintenance vehicles or garbage truck fleets. Driving is conducted on a daily basis for the council’s day to day work anyway, meaning the camera is able to assess the road without the council going out of their way.
The main way that the service is conducted is through vehicle fleets. Garbage fleets drive the road network multiple times a week meaning roads are driven multiple times at different times. If glare causes issues in one run of the road, it is generally picked up by another vehicle at a different time.
Road surface defects are very different from each other. All cracks have different shapes, sizes, depths and positions which requires a very large amount of effort and information to be fed into the machine learning model to improve the algorithm. The accuracy is always improved through regular training.
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