This paper presents a multi-agent license plate recognition system, specifically designed to address the diverse and challenging nature of license plates. Utilizing a multi-agent architecture with agents operating in individual Docker containers and orchestrated by Kubernetes, the system demonstrates remarkable adaptability and scalability. It leverages advanced neural networks, trained on a comprehensive dataset, to accurately identify various license plate types under dynamic conditions. The system’s efficacy is showcased through its three-layered approach, encompassing data collection, processing, and result compilation, significantly outperforming traditional license plate recognition (LPR) systems. This innovation not only marks a technological leap in license plate recognition but also offers strategic solutions for enhancing traffic management and smart city infrastructure globally.
Collaboration network of the authors
16 authors ·
42 co-authorship links
· 19 across different organisations
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University of Hassan II Casablanca
Université Ibn-Tofail
Université Hassan 1er
Abdelmalek Essaâdi University
Different organisations
Same organisation
Affiliation unknown
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