Research on the Application of Deep Learning Methods in the Field of Image Classification
With the rapid development of image classification technology, it has become a current research hotspot to apply image classification technology to various fields and to improve the accuracy and efficiency of image classification technology in various fields. In the field of fruit classification and...
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04036.pdf |
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| Summary: | With the rapid development of image classification technology, it has become a current research hotspot to apply image classification technology to various fields and to improve the accuracy and efficiency of image classification technology in various fields. In the field of fruit classification and textile, the application of image classification technology has been widely concerned. This paper reviews the current research status of image classification models, focusing on the application of DenseNet-201, Xception, MobileNetV3-Small and ResNet-50 models in the fruit field. The application of deep learning methods such as Convolutional Neural Network, Recurrent Neural Networks and Long Short-Term Memory in image classification is also discussed. In this paper, it is concluded that these models have achieved high accuracy in fruit classification and the textile field, especially the combination of CNN, RNN and LSTM deep learning methods for feature fusion can enhance the accuracy and robustness of the model. In addition, this paper also discusses the limitations of the current research and makes some suggestion. |
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| ISSN: | 2271-2097 |