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首页> 外文会议>International Conference on Machine Vision >Convolutional Neural Network with Transfer Learning for Rice Type Classification
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Convolutional Neural Network with Transfer Learning for Rice Type Classification

机译：随着稻米类型分类的转移学习卷积神经网络

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摘要

Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2-class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.
机译：目前，水稻类型由人手动鉴定，这是耗时和易于易于的。因此，需要通过机器进行这一点，这使得更快的精度更快。本文提出了一种基于深度学习的水稻类型分类方法。我们提出了两种分类米类型的方法。在第一种方法中，我们使用给定分段米图像训练深度卷积神经网络（CNN）。在第二种方法中，我们训练普雷雷达的VGG16网络和所提出的方法的组合，同时使用转移学习，其中使用预磨平网络的权重来实现更好的准确性。我们的方法也可用于分类米粒破碎或罚款。我们培养5级模型用于使用4000次训练图像和另外2级模型进行分类水稻类型，用于使用1600次训练图像进行分类和普通米的分类。我们观察到，尽管具有不同的米形图像，我们的架构，在想象特数据上预先磨削，显着提高了分类精度。

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