Scientific Journal of Silesian University of Technology. Series Transport Zeszyty Naukowe Politechniki Śląskiej. Seria Transport Volume 112 2021 p-ISSN: 0209-3324 e-ISSN: 2450-1549 DOI: https://doi.org/10.20858/sjsutst.2021.112.16 Journal homepage: http://sjsutst.polsl.pl Article citation information: Trivedi, J., Devi, M.S., Dhara, D. Vehicle classification using the convolution neural network approach. Scientific Journal of Silesian University of Technology. Series Transport. 2021, 112, 201-209. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2021.112.7.16 Janak TRIVEDI 1 , Mandalapu Sarada DEVI 2 , Dave DHARA 3 VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH Summary. We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset’s different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic. Keywords: convolution neural network, vehicle classification, deep learning, intelligent transportation system 1 Faculty of Electronics & Communication Engineering Department, Gujarat Technological University, Government Engineering College Bhavnagar-364002, Gujarat, India. Email: Trivedi_janak2611@yahoo.com. ORCID: 0000-0002-8662-5153 2 Principal, Ahmedabad Institute of Technolog-380060, Gujarat Technological University, Gujarat, India. Email: saradadevim1@gmail.com. ORCID: 0000-0003-4904-1906 3 Faculty of Electronics & Communication Engineering Department, Gujarat Technological University, Government Engineering College Bhavnagar-364002, Gujarat, India. Email: dave.dhara24888@gmail.com. ORCID: 0000-0002-7724-800X