Autonomous motor vehicle categorisation using a convolutional neural network

Abstract

We present an automatic approach in recognising the classification type of motor vehicles. Over the years, governments and private organisations have implemented schemes in which motor vehicles are categorised according to their size, weight, scale, and axel. Such schemes are used for various purposes, mainly for the implementation of new regulations, generation of statistics as well as urban development. This investigation proposes an autonomous system that is able to detect motorcycles, light motor vehicles (LMV’s) and heavy motor vehicles (HMV’s) from static images. This was established by training a Convolutional Neural Network (CNN) on preprocessed subsets of an existing dataset using the transfer learning technique. Our model achieved an F1 Score of 94.27% and an Accuracy Rate of 89.28% and challenging scenarios were identified.