Abstract
As the rate of data generation is growing rapidly which can be from a number of sources. Information collected can be used for and processed for its commercial or business value. Here, one of the characteristics is the significance of data in terms of time. In time-dependent applications, the need for analysis and quick processing is a necessity. Using YOLO (you look only once) method, we have performed recognition of vehicle type in which for each of the objects the model is trained. Here, the dataset used is from cityscapes where 2659 input set images are taken for training purpose and the performance is calculated in terms of accuracy which is 87%. Using the approach detection for different vehicle categories, i.e., car, bus, truck, motorbike is performed. YOLO model works well and does not require any intrusive approach for detection also due to less to no dependency on any other system optimization and reliability is attained.
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Bhujbal, A., Mane, D.T. (2020). Vehicle Type Classification Using Deep Learning. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_26
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DOI: https://doi.org/10.1007/978-981-15-2475-2_26
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