使用NVIDIA数字的清真标志分类

Hendrick, Chih-Min Wang, Aripriharta, Ciou-Guo Jhe, Ping-Chong Tsu, G. Jong
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引用次数: 5

摘要

深度学习在人脸检测、人脸识别、物体检测、手势检测等图像处理应用方面发展迅速。深度学习的另一个应用是对交通标志、标志和字符的识别。对于穆斯林来说,在购买一些产品之前识别清真标志是很重要的。并不是每个国家的清真标志都一样。印尼和台湾的清真标志都不一样。在本研究中,采用深度学习对清真标识进行分类。分类基于具有GoogleLeNet架构的caffe框架。作为数据集,创建了清真标识和软饮料标识。本研究的目的是产生一个清真标识的深度学习预训练模型。预训练的模型将在清真标识识别的手机应用中实现。深度学习预训练模型的准确率为81.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Halal Logo Classification by Using NVIDIA DIGITS
Deep learning has a rapid development in image processing application such as face detection, face recognition, object detection and also gesture detection. The other application of deep learning is in the identification of the traffic signs, logo and characters. For Muslim, the halal logo is important to identify before buying some products. The Halal logo is not the same for every country. Both Halal logo Indonesia and Taiwan are different. In this research, the deep learning was applied to classify the halal logo. The classification is based on the caffe framework with GoogleLeNet architecture. As datasets, the halal logo and soft drink logo were created. The purpose of this study is to produce a deep learning pre-trained model of the halal logo. The pre-trained model will be implemented in mobile phone application in halal logo identification. The accuracy of the deep learning pre-trained model is 81.7 %.
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