The paper analyzes various approaches to identifying and recognizing license plates in intelligent transport networks. A deep learning model has been proposed for localizing and recognizing license plates in natural images, which can achieve satisfactory results in terms of recognition accuracy and speed compared to traditional ones. Evaluations of the effectiveness of the deep learning model are provided.

Keywords: VANET, intelligent transport networks, YOLO, city traffic management system, steganography, deep learning, deep learning, information security, convolutional neural network, CNN

The paper discusses a stegoalgorithm with localization of the embedding area in the YCbCr color space to protect images of a license plate, a vehicle from different angles, a traffic event, as well as issues of developing a software system that implements the stegoalgorithm. Image protection allows you to effectively implement the concept of multimodal interaction of socio-cyberphysical systems in an automotive self-organizing network. Evaluations of the effectiveness of the developed method are provided.

Keywords: VANET, intelligent transport networks, city traffic management system, steganography, information security, watermark