集成的卷积神经网络在智能冰箱果蔬识别中的应用

冰箱内物体繁多 且摆放随意，给基于图像分析的冰箱物体识别带来了很多挑战。本文提出一种用集成的卷积神经网络方法来解决冰箱食物种类识别问题。其基本思想是首先分别训练两个卷积神经网络，一个用于果蔬种类识别，一个用于果蔬的颜色识别，然后用一个多层感知器将两个独 立的网络集成进行分类训练。集成训练之后的模型能将两个网络的信息进行补偿和强化。本文 方法能有效提升颜色在物体识别中的主导作用，改善了由于遮挡、视角变化导致识别准确性不高的问题。最后通过对从冰箱获取大量真实的图片数据进行实验，验证了本文方法在解决智能冰箱物体识别问题的有效性。

Integrated Convolutional Neural Network and Its Application in Fruits and Vegetables Recognition of Intelligent Refrigerator

As an important part of the household appliances, the refrigerator becomes more intelligent. Object recognition of the food in a refrigerator is a key technology of a smart refrigerator. However, the foods in the refrigerator are diverse and disordered, which brings a lot of challenges to identify the varieties of foods. A method using an integrated convolutional neural network is proposed to solve this problem. The basic idea is that two convolutional neural networks are firstly trained separately. One is used to recognize the kinds of fruits and vegetables, the other is to recognize the color of them. Then, a multilayer perceptron is used to integrate the two independent networks to carry out classification. The two separate convolutional neural networks can complement and improve each other in the integrated network. In the method, color information, an important feature in the recognition, can be enhanced. The proposed structure also improves the recognition rate which is influenced by object occlusion and view variations. Finally, the effectiveness of the proposed method is validated on a dataset which contains a large amount of images obtained from a real situation of a refrigerator.