Open AccessCar Image Classification and Recognition
Author(s) -
Dhairya Shah
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.38336
Subject(s) - python (programming language) , convolutional neural network , computer science , artificial intelligence , deep learning , contextual image classification , artificial neural network , architecture , pattern recognition (psychology) , construct (python library) , ranging , machine learning , computer vision , image (mathematics) , art , telecommunications , visual arts , programming language , operating system
Vehicle positioning and classification is a vital technology in intelligent transportation and self-driving cars. This paper describes the experimentation for the classification of vehicle images by artificial vision using Keras and TensorFlow to construct a deep neural network model, Python modules, as well as a machine learning algorithm. Image classification finds its suitability in applications ranging from medical diagnostics to autonomous vehicles. The existing architectures are computationally exhaustive, complex, and less accurate. The outcomes are used to assess the best camera location for filming, the vehicular traffic to determine the highway occupancy. An accurate, simple, and hardware-efficient architecture is required to be developed for image classification. Keywords: Convolutional Neural Networks, Image Classification, deep neural network, Keras, Tensorflow, Python, machine learning, dataset