Mahyoub, M, Natalia, F, Sudirman, S, Liatsis, P and Jasim Al-Jumaily, AH (2023) Data Augmentation Using Generative Adversarial Networks to Reduce Data Imbalance with Application in Car Damage Detection. In: Proceedings - 2023 15th International Conference on Developments in eSystems Engineering (DESE) , 2023-J. pp. 480-485. (2023 15th International Conference on Developments in eSystems Engineering (DESE), 09-12 January 2023, Baghdad & Anbar, Iraq).

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Abstract

Automatic car damage detection and assessment are very useful in alleviating the burden of manual inspection associated with car insurance claims. This will help filter out any frivolous claims that can take up time and money to process. This problem falls into the image classification category and there has been significant progress in this field using deep learning. However, deep learning models require a large number of images for training and oftentimes this is hampered because of the lack of datasets of suitable images. This research investigates data augmentation techniques using Generative Adversarial Networks to increase the size and improve the class balance of a dataset used for training deep learning models for car damage detection and classification. We compare the performance of such an approach with one that uses a conventional data augmentation technique and with another that does not use any data augmentation. Our experiment shows that this approach has a significant improvement compared to another that does not use data augmentation and has a slight improvement compared to one that uses conventional data augmentation.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions:	Computer Science & Mathematics
Publisher:	IEEE
SWORD Depositor:	A Symplectic
Date Deposited:	12 Jun 2023 16:30
Last Modified:	12 Jun 2023 16:30
DOI or ID number:	10.1109/DeSE58274.2023.10100274
URI:	https://researchonline.ljmu.ac.uk/id/eprint/19756

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