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Data augmentation with generative adversarial networks for grocery product image recognition

Citation

Wei, Y and Xu, S and Tran, S and Kang, B, Data augmentation with generative adversarial networks for grocery product image recognition, Proceedings of the 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020, 13-15 December 2020, Virtual Conference, Online (Shenzhen, China), pp. 963-968. ISBN 978-1-7281-7709-0 (2020) [Refereed Conference Paper]

Copyright Statement

Copyright IEEE

DOI: doi:10.1109/ICARCV50220.2020.9305421

Abstract

Image recognition tasks have gained enormous progress with a tremendous amount of training data. However, it isn't easy to obtain such training datasets that contain numerous annotated images in the domain of grocery product recognition. A small number of training data always results in a less than stellar recognition accuracy. Here we attempt to address this challenge by using generative adversarial networks (GAN), which can generate natural images for data augmentation. This paper aims to investigate the feasibility of using GAN to create synthetic training data, and thus to improve grocery product recognition accuracy. In this work, different GAN variants and image rotation are employed to enlarge the fruit datasets. Then, we train the CNN classifier using different data augmentation methods and compare the top-1 accuracy results. Finally, our experiments demonstrate that Auxiliary Classifier GAN (ACGAN) has achieved the best performance, which obtains l.26%∼3.44% increase in recognition accuracy. As an additional contribution, the results show that the effectiveness of using generated data is very close to that of using real data, which in our best experimental case, are 93.85% and 94.25%, respectively.

Item Details

Item Type:Refereed Conference Paper
Keywords:grocery product recognition, generative adversarial network (GAN), convolutional neural network (CNN)
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Computer vision
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Wei, Y (Mr Yuchen Wei)
UTAS Author:Xu, S (Dr Shuxiang Xu)
UTAS Author:Tran, S (Dr Son Tran)
UTAS Author:Kang, B (Professor Byeong Kang)
ID Code:142615
Year Published:2020
Deposited By:Information and Communication Technology
Deposited On:2021-02-01
Last Modified:2021-06-09
Downloads:0

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