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Author: Kang Le

Affiliation: Sun Yat-sen University, China

Keyword(s): Product identification, Keypoint alignment, Neural network, Data augmentation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Traditional shelf auditing is a manual audit. With the development of computer vision and deep learning technology, it has become possible to use machine automatic image recognition instead of manual auditing. Existing product identification is based on the use of two-dimensional code recognition and radio frequency identification (RFID), which relies on hardware and is relatively expensive. The training data of product identification is difficult to collect. This paper proposes a product identification method based on convolutional neural network, and explores how to effectively obtain the product data sets. At the same time, it introduces the unsupervised keypoint detection alignment method for the product detection part, and proves that it can improve the correct rate of product identification.

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Paper citation in several formats:
Le, K. (2019). Product Identification Based on Unsupervised Detection Keypoint Alignment and Convolutional Neural Networks. In Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - CTISC; ISBN 978-989-758-357-5, SciTePress, pages 160-165. DOI: 10.5220/0008099201600165

@conference{ctisc19,
author={Kang Le.},
title={Product Identification Based on Unsupervised Detection Keypoint Alignment and Convolutional Neural Networks},
booktitle={Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - CTISC},
year={2019},
pages={160-165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008099201600165},
isbn={978-989-758-357-5},
}

TY - CONF

JO - Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - CTISC
TI - Product Identification Based on Unsupervised Detection Keypoint Alignment and Convolutional Neural Networks
SN - 978-989-758-357-5
AU - Le, K.
PY - 2019
SP - 160
EP - 165
DO - 10.5220/0008099201600165
PB - SciTePress