Case Study: AUDENASA

Objective:

The customer needed to classify the vehicles driving on its toll roads to be able to apply different tariffs. In total, the AI had to differentiate up to 7 types of vehicles depending on the number of axles, wheels, trailers, etc.

Challenge:

The different traffic speeds, the poor visibility of the inner wheels, and the various weather and lighting conditions represented a challenge when working with pictures.

Solution: 

4 different cameras were installed (two up two down) to teach the AI that it had to focus both on the axles and on the vehicle bodywork. Given the large quantity of data required to be able to cover all the possibilities, the active learning technology used was fundamental to catch the more complex, less frequent cases.

“There’s no doubt about it, Artificial Intelligence is affecting our day-to-day work, particularly in companies. In the search for alternatives to traditional methods, AI provides new focuses and opportunities that we should at least explore. ENAIA has helped us apply it in our business process. The result suggests very interesting technical and economic alternatives.”

Ángel Ayala

Head of Information Technology (IT) and Control, Audenasa