Revolutionizing Vehicle Damage Inspection: A Deep Learning Approach for Automated Detection and Classification

Onikepo Amodu, Adel Shaban, Gbenga Akinade

2024

Abstract

In the past, fleet managers and vehicle insurance companies relied on manual methods to inspect vehicle damage. This involved visually examining the vehicles and taking measurements manually. The aim of this study was to explore the use of deep learning algorithms to automate the process of vehicle damage detection and classification. By automating these tasks, stakeholders in the industry, such as fleet managers and insurance companies, can streamline vehicle inspections, assess the extent and severity of damage, and validate insurance claims. The research focused on three main deep learning architectures: Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Deep Neural Networks (DNNs). These algorithms were applied to a diverse dataset containing vehicles in different lighting conditions. The study conducted a comprehensive evaluation of each algorithm’s performance, considering factors such as accuracy, speed, and detection rates. The goal was to assess the strengths and weaknesses of each approach. The results of the experiment revealed significant differences in the performance of the CNN, DNN, and GAN models. The CNN model achieved the highest accuracy rate, at 91%, followed by the DNN model at 84%. The GAN model achieved a more modest accuracy rate of 78%. These findings contribute to the advancement of vehicle damage detection technology and have important implications for industries, policymakers, and researchers interested in deploying state-of-the-art solutions for faster and more precise identification of various levels of damage and their severity.

Paper Citation

in Harvard Style

Amodu O., Shaban A. and Akinade G. (2024). Revolutionizing Vehicle Damage Inspection: A Deep Learning Approach for Automated Detection and Classification. In Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS; ISBN 978-989-758-699-6, SciTePress, pages 199-208. DOI: 10.5220/0012630700003705

in Bibtex Style

@conference{iotbds24,
author={Onikepo Amodu and Adel Shaban and Gbenga Akinade},
title={Revolutionizing Vehicle Damage Inspection: A Deep Learning Approach for Automated Detection and Classification},
booktitle={Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS},
year={2024},
pages={199-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012630700003705},
isbn={978-989-758-699-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS
TI - Revolutionizing Vehicle Damage Inspection: A Deep Learning Approach for Automated Detection and Classification
SN - 978-989-758-699-6
AU - Amodu O.
AU - Shaban A.
AU - Akinade G.
PY - 2024
SP - 199
EP - 208
DO - 10.5220/0012630700003705
PB - SciTePress