Pole position for an AI-system to ‘see’ stationary objects in the environment

15 January 2021
Pole position for an AI-system to ‘see’ stationary objects in the environment

Posted: 30/07/19

Telegraph poles – you probably pass them all the time, yet barely notice them unless they have fallen. For eir, though, keeping track of those all-important poles and the connections they enable is crucial. That’s why they asked researchers at SFI Research Centre ADAPT, Trinity College Dublin, to develop artificial intelligence that can help them improve their database of poles.

“Eir has a digitised inventory of poles, but they wanted to check the information was correct, they knew there could be errors,” explains Professor Rozenn Dahyot, an Associate Professor in Statistics at Trinity College Dublin.

The team at SFI Research Centre ADAPT investigated approaches to improve the quality of data about telegraph poles without the need for humans to be physically present at the pole location, but they found that some perspectives would not work well.

“We couldn’t use data from satellite images or Lidar 3D data, because when you look from above the part of the pole you see is very small and the resolution was not good enough to be able to tell much,” explains Dr Eamonn Kenny, ADAPT Research Fellow responsible for data management in the project.

Instead, they found that Street View imagery offered a more useful dataset, because it contained images of roads captured at car level. “This gave us a 360-degree view from each point, and we had GPS information about where the car was as well as which views were north, south, east and west. It contained a lot of useful information.”

One of the challenges in the project though was to ensure that the same pole wasn’t counted multiple times in the street views. The team developed artificial intelligence algorithms or computer code to avoid this, as Professor Dahyot explains, “It was important to ensure that each pole was uniquely identified across several images, we used deep-learning and other techniques from statistics and machine learning to optimise the accuracy of the results.”

To find out how well the new computer programs worked, they ran them on areas where the answers were already known, explains Dr Vladimir Krylov, ADAPT Research Fellow responsible for the AI image processing pipeline on the project. “We were given access to the records that eir had for areas where they knew the information was accurate. So we ran the algorithms in those areas to calibrate the system.”

The year-long AIMapIT project (Automatic Detection and Geotagging of Stationary Objects from Street Level Imagery) was hailed as a great success, with former eir CTO Helene Graham suggesting that the economic value of the initial use had been estimated at €3m over a three-year period. The research was also shortlisted for an IBEC Digital Technology Award Ireland in 2017  for Outstanding Academic Achievement and For the AI Awards Ireland 2018 under the title of Best Contribution To AI – Academic Research Body.

The ADAPT team has been working with Enterprise Ireland to explore the feasibility of commercialising the approach for use in other situations, explains Professor Dahyot, who sees the potential for many other applications. “There are lots of uses for the precise geolocation and tagging of objects, including 3D-mapping technologies, road signage, autonomous vehicles and even drone deliveries: you don’t want the drone delivering your package to the neighbour!”

Originally Featured by Science Foundation Ireland on www.sfi.ie

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