Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 79, Issue 21-22, Pages 15117-15133Publisher
SPRINGER
DOI: 10.1007/s11042-018-6661-6
Keywords
Deep learning; Computer vision; Convolutional neural network; Data augmentation; Dropout; Fruit category classification; Parametric rectified linear unit
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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.
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