Abstract
In this paper, we apply an improved deep convolutional neural network (CNN) in fruit category classification, which is a hotspot in computer vision field. We created an 8-layer deep convolutional neural network, and utilized parametric rectified linear unit to take the place of plain rectified linear unit, and placed dropout layer before each fully-connected layer. Data augmentation was used to help avoid overfitting. Our 8-layer deep convolutional neural network secured an overall accuracy of 95.67%. This proposed 8-layer method performs better than five state-of-the-art methods using traditional machine learning methods and one state-of-the-art CNN method.
Original language | English |
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Pages (from-to) | 15117-15133 |
Number of pages | 17 |
Journal | Multimedia Tools and Applications |
Volume | 79 |
Issue number | 21-22 |
DOIs | |
Publication status | Published - 1 Jun 2020 |
Externally published | Yes |
Keywords
- Computer vision
- Convolutional neural network
- Data augmentation
- Deep learning
- Dropout
- Fruit category classification
- Parametric rectified linear unit