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The fruit classification algorithm based on the multi-optimization convolutional neural network
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DOI：10.1007/s11042-020-10406-6.png)
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Abstract
En 中文
To solve the problems of the traditional convolutional neural network's needs of long training time and poor accuracy in the process of fruit image classification, the present study proposes a fruit image classification method based on the multi-optimization convolutional neural network with the background of fruit classification. Firstly, in order to avoid the interference of external noise and influence the accuracy of classification, the wavelet threshold is used to denoise the fruit image, which can reduce image noise while preserving the details of the image. Secondly, to correct the over-bright fruit image or the over-dark fruit image, the gamma transform is adopted to correct the image. Finally, in the process of constructing the convolutional neural network, the SOM network is introduced for pre-learning the samples. Besides, the weights of the trained optimal SOM network are applied to the full connection layer, and an integrated optimization model of convolution and full connection is established for feature extraction and regression classification. The optimized convolutional neural network was adopted to classify fruits. According to the application results, the accuracy of the optimized convolutional neural network for fruit classification reaches 99%. Therefore, the improved convolutional neural network depth learning algorithm makes better performance to achieve fruit classification.
Keywords:
Convolutionnal neural network
Deep learning
Image classification
Linear integration optimization
Weight initialization
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W
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