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Abstract
Research paper explores the use of computer vision technologies to automatically classify vehicles in the new toll collection systems. Specifically, the analysis focuses on integrating deep learning algorithms, especially convolutional neural networks (CNN), into modern toll plaza operations. Research shows that vision-systems technology improves the speed of processing and classification accuracy rates to levels that are previously unattainable. The research aids in understanding how these systems can be implemented, the challenges posed, and the prospects for practical widespread usage. The results achieved in this study suggest the strong need for the investment in improving the operational cost effectiveness, system cost, and overall satisfaction of the users of the toll collection systems.