Design And Implementation Of Household Waste Classification System Based On Deep Learning

China’s development has entered a new era,in which the concept of development is innovation,coordination,green development,openness and sharing.Green development focuses on solving the problem of harmony between man and nature,advocating the classification of household waste and recycling garbage resources,which is conducive to reducing land erosion and environmental pollution.However,as there are many kinds of household waste and people do not have a comprehensive understanding of the household waste classification standards,household waste classification often gets half the result with twice the effort.This paper designs a household waste classification system based on the improved ResNeXt50 network model,and uses deep learning method to classify garbage by computer instead of human brain,so as to help people improve the efficiency and quality of garbage classification.The main works of this paper are as follows:(1)In view of the problem that there are not many garbage types and few sample pictures in huawei garbage classification public data set,this paper makes a household garbage classification data set with 30848 sample pictures of 64 garbage types on its basis.Four mainstream image classification network models,AlexNet,VGG16,GoogLeNet and ResNet50,were built.The training effect was compared on the household waste classification data set,and the ResNet series with better classification effect was selected.On this basis,the ResNeXt model is built,and the classification accuracy of the model can reach 80%;(2)In view of the low classification accuracy of ResNeXt basic model,this paper makes the following improvements: In ResNeXt model 50-Layer and 101-Layer,transfer learning technology based on ImageNet data set is used.The network training results show that transfer learning can make ResNeXt50 achieve higher classification accuracy on the household waste classification data set.In order to enhance the ability of extracting image feature information from the model,the attention mechanism in SENet was integrated into the residual network structure of ResNeXt50 to build the SE-ResNeXt50 model,which further improved the classification accuracy of the model.In order to accelerate the training and convergence speed of network model and improve the training effect of the model,this paper also proposed a Adam optimization algorithm with exponential dynamic learning rate,and verified the effect in the training of the model.Finally,the classification accuracy of SE-ResNeXt50-lr network model designed on the household waste classification data set reached 96.23%.Garbage image can be accurately identified and classified;(3)To solve the problem that users cannot directly operate the network model for garbage classification,this paper uses Tkinter library to design the visual front-end operation interface of the household waste classification system,control the back-end network model and transmit data,and debug the front and back ends of the system respectively.This paper finally designs the household waste classification system based on the SE-ResNeXt50-lr network model,and verifies the feasibility and practicability of the system through the application test in the real environment.The system can help people better solve the household waste classification problem,achieve the expected goal of the project,and meet the actual application requirements.