Design And Development Of Garbage Classification System Based On Deep Learning

With the improvement of the level of social and economic development,the improvement of people’s material living standards,the increase of population and urban population density,the amount of waste output is also increasing year by year,and its impact on environmental pollution and people’s health has become increasingly prominent.In response to this situation,our country has begun to implement waste classification.Garbage classification can give full play to the use value of resources and production materials,and reduce the impact of human production and economic activities on the natural world.But for a wide variety of garbage,how to achieve accurate classification is a thorny new problem.In addition,with the continuous in-depth research on deep learning technology and the continuous improvement of hardware computing power,more and more problems can be solved based on deep learning models,especially in the application of visual algorithm models,which has achieved unprecedented breakthroughs..Therefore,based on deep learning technology,an image classification model can be designed to solve the current difficult problem of garbage classification.The specific work completed in this paper is as follows:First,modify the Huawei garbage data set to make it suitable for the needs of the system.Then designed the EfficientNet as the backbone network,combined with the attention module CBAM network model,CBAM makes the network model focus on the target to be recognized,and extract more effective features.In order to solve the problem of unbalanced data distribution and improve the generalization ability of the model,a series of data enhancement strategies are adopted,and the model loss function is modified to improve the accuracy of the model.Then,based on the classification model,we designed and implemented the We Chat applet service of C/S architecture and the Web back-end management system of B/S architecture.Among them,users can submit images or retrieve objects through the We Chat applet,and query the corresponding classification results.Regarding the error of garbage identification,the user can give feedback on the image and submit it to the web management terminal for review.The administrator can log in to the Web back-end system to review and annotate user feedback images and annotate the data set to be trained to complete the subsequent optimization of the model.Finally,experiments were conducted on the classification model designed in this paper on the verification set and the collected test set.The experimental results verified the effectiveness of the model and high accuracy;the entire garbage classification system was tested functionally and non-functionally.The results show that the system meets the needs of use and has high practicability.