YOLO BASED TINY VEHICLE DETECTION
Keywords:
Smart city applications, Deep Neural Network, Parking occupancy detection, YOLO-v5 architecture, Object detection model, Multi-scale mechanism, Discriminative feature representations, Trainable parameters, Detection speed, Tiny vehicle detection.Abstract
To solve real-life problems for different smart city applications, using deep Neural Network, such asparking occupancy detection, requires fine-tuning of these networks. For large parking, it is desirable to use a
cenital-plane camera located at a high distance that allows the monitoring of the entire parking space or a large
References
S.B. Atitallah, M.Driss, W.Boulila, and H.B.Ghézala, “Lever aging deep learning an diot big data analytics to support the smart cities
development: Review and future directions,” Computer Science Review, vol. 38, p. 100303, 2020
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Published
2024-02-15
How to Cite
DR.V.ANANTHA KRISHNA , RAVIRALA AKARSHA, BHATRAJU DEEPIKA, TABASSUM FATHIMA. (2024). YOLO BASED TINY VEHICLE DETECTION . Journal of Computational Analysis and Applications (JoCAAA), 32(2), 387–395. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2713
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