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
