Abstract
Vehicle-type classification is fast becoming a popular domain of interest due to its various application areas ranging from traffic surveillance to autonomous navigation. In this paper, customized fuzzy convolutional neural network (CFCNN) is proposed for the classification of vehicle types by incorporating a customized convolutional neural network (CCNN) with a fuzzy hypersphere neural network (FHSNN). The two pivotal factors for the improvement of the CFCNN learning algorithm are: first, the ability to extract dominant hidden features from vehicle front raw images using CCNN and second, fuzzy hypersphere membership function used for pattern classification. The proposed model's performance evaluated using the BIT-vehicle standard dataset, which consists of high-resolution 9850 images of various five types of vehicles, and the testing accuracy obtained 94.26%, which is exceptionally better in comparison to the existing algorithms.
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Mane, D.T., Kumbharkar, P.B., Dhotre, P.S., Borde, S. (2021). Vehicle-Type Classification Using Customized Fuzzy Convolutional Neural Network. In: Bhateja, V., Satapathy, S.C., Travieso-González, C.M., Aradhya, V.N.M. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 1407. Springer, Singapore. https://doi.org/10.1007/978-981-16-0171-2_40
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DOI: https://doi.org/10.1007/978-981-16-0171-2_40
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