Title: Deep learning solution for machine vision problem of vehicle body damage classification

Authors: Aaron Rasheed Rababaah

Addresses: College of Engineering and Applied Sciences, American University of Kuwait, Salmia, Kuwait

Abstract: The automation of vehicle damage classification into classes of interest has benefits over manual solutions such as efficiency, accuracy, reliability and repeatability. Industries such as automotive dealerships, car rentals and car insurance are among the most industries that are expected to be interested in such a solution. In this paper, we present machine vision and deep learning-based method for vehicle damage classification based on convolution neural networks (CNNs) models. For training and validation, we used a publicly available dataset along with our own to increase input data as CNN models require significantly much more data than classical machine learning models. Our best performing model demonstrated a remarkable classification accuracy of 98.7%. As future work, we intend to consider a wider range of damage classes and significantly extend the current dataset to further validate the current solution.

Keywords: vehicle damage classification; image processing; machine vision; deep learning; convolutional neural networks.

DOI: 10.1504/IJCVR.2022.123853

International Journal of Computational Vision and Robotics, 2022 Vol.12 No.4, pp.426 - 442

Received: 15 May 2021
Accepted: 11 Jul 2021
Published online: 04 Jul 2022 *

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