| 000 | 02696nla2a2200469 4500 | ||
|---|---|---|---|
| 001 | 661313 | ||
| 005 | 20231030041742.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\31638 | ||
| 035 | _aRU\TPU\network\31636 | ||
| 090 | _a661313 | ||
| 100 | _a20191128a2019 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aGB | ||
| 105 | _ay z 100zy | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aA fruits recognition system based on a modern deep learning technique _fDang Thi Phuong Chung, Dinh Van Tai |
|
| 203 |
_aText _celectronic |
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| 300 | _aTitle screen | ||
| 320 | _a[References: 7 tit.] | ||
| 330 | _aThe 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 | 0 |
_0(RuTPU)RU\TPU\network\3526 _tJournal of Physics: Conference Series |
|
| 463 | 0 |
_0(RuTPU)RU\TPU\network\31502 _tVol. 1327 : Innovations in Non-Destructive Testing (SibTest 2019) _oV International Conference, 26–28 June 2019, Yekaterinburg, Russia _o[proceedings] _fNational Research Tomsk Polytechnic University (TPU) _v[012050, 5 р.] _d2019 |
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| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aсистемы распознавания | |
| 610 | 1 | _aфрукты | |
| 610 | 1 | _aкомпьютерное зрение | |
| 610 | 1 | _aалгоритмы | |
| 610 | 1 | _aмашинное обучение | |
| 610 | 1 | _aидентификация | |
| 610 | 1 | _aизображения | |
| 610 | 1 | _aнейронные сети | |
| 610 | 1 | _aсверточные нейронные сети | |
| 700 | 0 | _aDang Thi Phuong Chung | |
| 701 | 0 | _aDinh Van Tai | |
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _c(2009- ) _2stltpush _3(RuTPU)RU\TPU\col\15902 |
| 801 | 2 |
_aRU _b63413507 _c20200123 _gRCR |
|
| 856 | 4 | _uhttps://doi.org/10.1088/1742-6596/1327/1/012050 | |
| 856 | 4 | _uhttp://earchive.tpu.ru/handle/11683/57042 | |
| 942 | _cCF | ||