Iranian Firm Indigenizes License Plate Reading, Speedometer Cameras
19:30 - August 07, 2023

Iranian Firm Indigenizes License Plate Reading, Speedometer Cameras

TEHRAN (ANA)- Researchers of a knowledge-based company in Iran succeeded in indigenizing speedometer and license plate reading cameras by making vision devices and using artificial intelligence.
News ID : 3251

“These cameras are made with the goal of car color recognition, license plate reading (cameras), car type recognition and speedometer,” Behzad Naderi, a representative of the knowledge-based company, told ANA.

He explained that the new cameras work differently from CCTV (Closed-circuit television) and surveillance systems and are specially made for this goal.

“We succeeded in designing and manufacturing industrial laser cameras with high precision and these products are used in different industries like steel industry and automobile industry,” Naderi said.

He underlined that indigenization of these cameras will reduce human error to zero and saves time.

In a relevant development in July, a new robot was developed by Iranian researchers at a knowledge-based company which can verify the registered traffic fines and reduce human error of the police.

“Our company started its activity in the field of artificial intelligence in 2018 and was founded by a group of elites active in the scientific and industrial fields,” Farhad Sadri, a representative of the knowledge-based company, told ANA.

“We use the police robot to improve traffic programs. When a traffic control camera records a fine, it will then become validated by the police robot before it is enforced,” he added

Sadri explained that the smart robot matches the appearance of the car in the photo with the specifications requested from the police system and provides the results accurately and rapidly, adding when the robot is not sure about the declaration of approval or discrepancy to issue a fine, it refers the issue to the check department’s operator.

Noting that the license plate reading errors are reduced by using the robot, he said that it is estimated that this error has decreased to below 5%.

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