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Deep Learning Approach for Cosmetic Product Detection and Classification

Se-Won Kim1, Sang-Woong Lee2,*

1 Department of IT Convergence Engineering, Gachon University, Seongnam, 13120, Korea
2 Department of Software, Gachon University, Seongnam, 13120, Korea

* Corresponding Author: Sang-Woong Lee. Email: email

Computers, Materials & Continua 2021, 69(1), 713-725.


As the amount of online video content is increasing, consumers are becoming increasingly interested in various product names appearing in videos, particularly in cosmetic-product names in videos related to fashion, beauty, and style. Thus, the identification of such products by using image recognition technology may aid in the identification of current commercial trends. In this paper, we propose a two-stage deep-learning detection and classification method for cosmetic products. Specifically, variants of the YOLO network are used for detection, where the bounding box for each given input product is predicted and subsequently cropped for classification. We use four state-of-the-art classification networks, namely ResNet, InceptionResNetV2, DenseNet, and EfficientNet, and compare their performance. Furthermore, we employ dilated convolution in these networks to obtain better feature representations and improve performance. Extensive experiments demonstrate that YOLOv3 and its tiny version achieve higher speed and accuracy. Moreover, the dilated networks marginally outperform the base models, or achieve similar performance in the worst case. We conclude that the proposed method can effectively detect and classify cosmetic products.


Cite This Article

S. Kim and S. Lee, "Deep learning approach for cosmetic product detection and classification," Computers, Materials & Continua, vol. 69, no.1, pp. 713–725, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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