Abstract
A university campus has a lot of small elements to manage. It is tough to administer these campuses effectively when all you have is human power and ordinary methods. An intelligent system, on the other hand, might be beneficial. The foundation of this system is human interaction with physical space and things. It will make such regions less time-consuming and labor-intensive to monitor. In this research work, we present a practical solution for a vehicle recognition model for smart universities based on Deep Convolutional Neural Networks (CNN) with the use of You Only Look Once (YOLO) version 5 network, which are commonly employed in speed of identification applicable in real-time object identification in smart cameras. Using data sources created in this study environment, this solution is compared to current methods. Our initial experiments demonstrate that the solution is superior to the generalized techniques on both data sets. As a result, it demonstrates the viability of the suggested system.
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Tra, H.T.H., Trung, H.D., Trung, N.H. (2022). YOLOv5 Based Deep Convolutional Neural Networks for Vehicle Recognition in Smart University Campus. In: Abraham, A., et al. Hybrid Intelligent Systems. HIS 2021. Lecture Notes in Networks and Systems, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-030-96305-7_1
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DOI: https://doi.org/10.1007/978-3-030-96305-7_1
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