Dual Convolution Neural Networks of Ensemble Learning with Attention Mechanism for Rice Classification
Machine vision has been widely applied across fields. Image classification is one of the most classic fields. The aim of this project is to develop a dual convolutional neural network for ensemble learning based on the initial model and the res network model, and apply the ensemble model to the rice...
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| Main Author: | |
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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_03025.pdf |
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| Summary: | Machine vision has been widely applied across fields. Image classification is one of the most classic fields. The aim of this project is to develop a dual convolutional neural network for ensemble learning based on the initial model and the res network model, and apply the ensemble model to the rice classification problem. The ensemble model in this article combines two deep models, InceptionNet and ResNet, and incorporates self-attention block method to construct an attention mechanism that uses multi head attention layers to capture relationships in the input. Attention output is added back to input. At the same time, 10 evaluation indicators were introduced as the results of testing and evaluation. In the result analysis, it can be concluded that the ensemble model has demonstrated excellent training efficiency in these indicators, and the learning rate hyperparameter has been replaced to improve the stability of the model. At the same time, for a more comprehensive comparison, the ensemble model studied in this article was also compared and analyzed reasonably with three pre trained models: VGG-16, ResNet50, and MobileNet. In the future, it is necessary to continuously optimize the structure of integrated models and adjust their hyperparameters to achieve better stability. |
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| ISSN: | 2271-2097 |