基于CNN和Group Normalization的校园垃圾图像分类

Campus Garbage Image Classification Method Based on CNN and Group Normalization

为解决大学校园的垃圾回收分类问题,提出了一种基于卷积神经网络和归一化技术的垃圾图像分类方法,不需要对输入的图像进行复杂的处理,网络模型即可根据算法提取图像特征,通过加入群组归一化(Group Normalization)和网络模型各层之间的协作,克服传统分类算法的缺点,实现对垃圾图像的分类。实验表明,该识别方法具有较高准确率,可以较好识别不可回收及可回收垃圾。

In order to solve the problem of waste classification in university campus,a method of garbage image classification based on convolution neural network and normalization technology is proposed. Without complex processing of the input image,the network model can extract image features according to the algorithm. By cooperating group normalization and each layer of the network model,the shortcomings of the traditional classification algorithm can be overcome and the garbage image can be classified. The final recognition has a high accuracy rate,and can identify unrecyclable garbage and recyclable garbage.