In their role as road operators, councils deliver safe and reliable road infrastructure to the community. Detecting and repairing defects, like potholes, pavement cracking, damaged guardrails and faded line marking is essential to achieve this. Councils repair thousands of defects each year.
Defects are reported by the community or by council staff inspecting the network. As a result, defects are only known when they already pose a risk to the community. Early detection of road defects will improve maintenance planning, reduce risks for road users and ultimately assist Council to deliver better road infrastructure to the community.
Currently, no systems are implemented that automate this process.
The implementation of Artificial Intelligence (AI) based defect detection technologies is an emerging market that will have wide ranging impacts to the management of road and pavement assets, particularly for local government agencies tasked with their maintenance and upkeep.
In this trial we tested technology that uses video footage and machine learning algorithms for asset identification and to detect road defects. The cameras can be mounted to council vehicles like garbage collection trucks and vehicles from inspectors or rangers, that already drive the network on a regular basis.
Visual assessment and validation of the data showed over 70% accuracy in defect identification, which will be compared more closely in future evaluations. Based on the evaluation, each of the systems offers potential applications to which they could be suited. Further developments will improve the outcomes generated for each system.
The Report is available for download with member council accesss or by registering for free.
To showcase the findings of this Evaluation, the Roads and Transport Directorate held a complimentary online webinar on Wednesday 11th August, 10:30am to 12pm.
Attendees joined us to hear first-hand about the trial of this game-changing technology and specifically what it means for councils in advancing asset data collection of their road networks.
A recording of the webinar is available here.
View the webinar presentation slides here.