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Urban Pothole Detection - Use Case





Cost of Potholes in the Urban Environment

A country such as Canada, where the temperature fluctuates on a regular basic, increases the odds of potholes on highway/motorways. Increased number of potholes not only lead to higher infrastructure cost for the government but also have adverse effects on vehicles.

  • For every $1 not spent on pothole maintenance can in theory lead to $7 of cost in the next five [1].

  • Rebuilding of the road can cost 14 times the cost required to fill potholes [1].

  • Every year 3 billion dollars are spent by car drivers across Canada on vehicle repairs caused due to potholes [2].


 

Detecting Potholes on a Macro Scale


Leveraging the current public transport system in Canada paired with ML/AI and satellites to pinpoint the location of detected potholes and reporting them directly based on their priority.


  • Canada has over 15,000 urban transit busses on which ML/AI camera can be placed to detect potholes [3].


Currently the government is heavily reliant on the public to call in on the general helpline to log any potholes within their community. This approach, even though widely popular, can lead to many false/low priorities cases. Automating the process by leveraging EdgeFirst vision technologies provided by Au-Zone, communities will be much safer and reduce inspection cost which will in turn increas0es driver satisfaction.


 

EdgeFirst Vision Applications for Detecting Potholes

With state-of-the-art models and a vision accelerated pipeline, Au-Zone’s AI Middleware can be used in a variety of circumstances to detect and report potholes on city streets.


Our hardware combined with our model training algorithms are capable of detecting objects allowing for the vision sensors to be attached to public transit for edgefirst object detection. Which can scan the roads for potholes in real time and alert the end user based on set priority.

  • With state-of-the-art models and a vision accelerated pipeline, Au-Zone’s AI Middleware can be used in a variety of circumstances to detect and report potholes on city streets.

  • Detection and classification of potholes to determine the size, severity, and location of potholes within an environment

  • EdgeFirst compute, along with an accelerated vision pipeline for immediate connectivity and low latency.

  • Realtime GPS connectivity for precise location tracking.

  • Scalable object detection to ensure public safety and monitoring.



Go from idea to concept to production faster and with more confidence using our EdgeFirst DeepView AI Middleware and Vision Starter Kits. For more information please click here or visit www.edgefirst.ai