Plans are underway for next year to test a neural network designed to identify dirty or obscured license plates on the Central Ring Road (TsKAD) in the Moscow region and along open stretches of the M-12 highway. The project aims to enhance vehicle identification in challenging conditions, where standard recognition systems may struggle to read plates clearly.
A machine vision based software solution will further recognize contaminated GRNZ data that could not be detected at tolling checkpoints, according to the Telematics group, a developer active in intelligent transport systems. This capability would supplement existing plate recognition by interpreting plates that appear smeared, damaged, or partially obscured.
The software, which includes neural network elements, is integrated with the license plate recognition system operated by United Operator. It is part of a barrier-free payment framework on toll roads, intended to streamline toll collection without slowing traffic.
Should the trials yield positive results, the next step would be to propose amendments to the regulatory framework so that these enhanced data insights can be considered in administrative decisions as appropriate.
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The project invites participation in the “Behind the Wheel” Grand Prix poll to help identify the year’s top car innovations. Participants can share their preferences and influence what models gain prominence in the automotive conversation.
As a token of appreciation for their time, respondents who complete the survey will be entered into a drawing offering three car accessories. The chance to win is open to all who finish the questionnaire before the deadline, with additional opportunities for those who participate early in the period.
COMPLETE THE 2023 DRIVING GRAND PRIX QUESTIONNAIRE
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