Classification of Domestic Refuse in Medical Institutions Based on Transfer Learning and Convolutional Neural Network  

作　　者：Dequan Guo Qiao Yang Yu-Dong Zhang Tao Jiang Hanbing Yan 

机构地区：[1]School of Control Engineering,Chengdu University of Information Technology,Chengdu,610225,China [2]College of Automation,Chongqing University of Posts and Telecommunications,Chongqing,400065,China [3]Department of Informatics,University of Leicester,Leicester,LE17RH,UK

出　　处：《Computer Modeling in Engineering & Sciences》2021年第5期599-620,共22页工程与科学中的计算机建模（英文）

基　　金：This work was supported in part by the National Natural Science Foundation of China under Grant 61806028,Grant 61672437 and Grant 61702428;Sichuan Science and Technology Program under Grants 21ZDYF2484,2021YFN0104,21GJHZ0061,21ZDYF3629,21ZDYF2907,21ZDYF0418,21YYJC1827,21ZDYF3537,2019YJ0356;the Chinese Scholarship Council under Grants 202008510036,201908515022.

摘　　要：The problem of domestic refuse is becoming more and more serious with the use of all kinds of equipment in medical institutions.This matter arouses people’s attention.Traditional artificial waste classification is subjective and cannot be put accurately;moreover,the working environment of sorting is poor and the efficiency is low.Therefore,automated and effective sorting is needed.In view of the current development of deep learning,it can provide a good auxiliary role for classification and realize automatic classification.In this paper,the ResNet-50 convolutional neural network based on the transfer learning method is applied to design the image classifier to obtain the domestic refuse classification with high accuracy.By comparing the method designed in this paper with back propagation neural network and convolutional neural network,it is concluded that the CNN based on transfer learning method applied in this paper with higher accuracy rate and lower false detection rate.Further,under the shortage situation of data samples,the method with transfer learning and ResNet-50 training model is effective to improve the accuracy of image classification.

关 键 词：Domestic refuse image classification deep learning transfer learning convolutional neural network 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]