A fruits recognition system based on a modern deep learning technique
| Parent link: | Journal of Physics: Conference Series Vol. 1327 : Innovations in Non-Destructive Testing (SibTest 2019).— 2019.— [012050, 5 р.] |
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| 總結: | Title screen The popular technology used in this innovative era is Computer vision for fruit recognition. Compared to other machine learning (ML) algorithms, deep neural networks (DNN) provide promising results to identify fruits in images. Currently, to identify fruits, different DNN-based classification algorithms are used. However, the issue in recognizing fruits has yet to be addressed due to similarities in size, shape and other features. This paper briefly discusses the use of deep learning (DL) for recognizing fruits and its other applications. The paper will also provide a concise explanation of convolution neural networks (CNNs) and the EfficientNet architecture to recognize fruit using the Fruit 360 dataset. The results show that the proposed model is 95% more accurate. |
| 語言: | 英语 |
| 出版: |
2019
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| 主題: | |
| 在線閱讀: | https://doi.org/10.1088/1742-6596/1327/1/012050 http://earchive.tpu.ru/handle/11683/57042 |
| 格式: | 電子 Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=661313 |
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| 200 | 1 | |a A fruits recognition system based on a modern deep learning technique |f Dang Thi Phuong Chung, Dinh Van Tai | |
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| 320 | |a [References: 7 tit.] | ||
| 330 | |a The popular technology used in this innovative era is Computer vision for fruit recognition. Compared to other machine learning (ML) algorithms, deep neural networks (DNN) provide promising results to identify fruits in images. Currently, to identify fruits, different DNN-based classification algorithms are used. However, the issue in recognizing fruits has yet to be addressed due to similarities in size, shape and other features. This paper briefly discusses the use of deep learning (DL) for recognizing fruits and its other applications. The paper will also provide a concise explanation of convolution neural networks (CNNs) and the EfficientNet architecture to recognize fruit using the Fruit 360 dataset. The results show that the proposed model is 95% more accurate. | ||
| 461 | 1 | |0 (RuTPU)RU\TPU\network\3526 |t Journal of Physics: Conference Series | |
| 463 | 1 | |0 (RuTPU)RU\TPU\network\31502 |t Vol. 1327 : Innovations in Non-Destructive Testing (SibTest 2019) |o V International Conference, 26–28 June 2019, Yekaterinburg, Russia |o [proceedings] |f National Research Tomsk Polytechnic University (TPU) |v [012050, 5 р.] |d 2019 | |
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