Deep Learning Solution for Machine Vision Problem of Vehicle Body Damage Classificatio

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Rababaah, Aaron
Peer Reviewed
Journal Article
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.
Rababaah, A. R. (2022). Deep learning solution for machine vision problem of vehicle body damage classification. International Journal of Computational Vision and Robotics, 12(4), 426-442.