Mahyoub, Mohamed and Natalia, Friska and Sudirman, Sud and Liatsis, Panos and Al-Jumaily, Abdulmajeed Hammadi Jasim (2023) Data Augmentation Using Generative Adversarial Networks to Reduce Data Imbalance with Application in Car Damage Detection. In: 2023 15th International Conference on Developments in eSystems Engineering (DeSE).
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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) |
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| Keywords: | Training, Deep learning, Insurance, Manuals, Inspection, Generative adversarial networks, Data models |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods > 006.3 Artificial Intelligence, Machine Learning, Pattern Recognition, Data Mining 300 Social Sciences > 380 Commerce, communications and transportation > 388 Transportation (Road, Vehicle, Parking Facilities) |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 07 Nov 2023 00:47 |
| Last Modified: | 07 Nov 2023 00:47 |
| URI: | https://kc.umn.ac.id/id/eprint/27050 |
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