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MSWNet:A visual deep machine learning method adopting transfer learning based upon ResNet 50 for Municipal solid waste sorting

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Frontiers of Environmental Science & Engineering 2023年第6期17卷 165-176页

作者： Kunsen Lin Youcai Zhao Lina Wang Wenjie Shi Feifei Cui Tao Zhou The State Key Laboratory of Pollution Control and Resource Reuse College of Environmental Science and EngineeringTongji UniversityShanghai 200092China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science and EngineeringFudan UniversityShanghai 200433China Institute of Eco-Chongming(IEC) Shanghai 202150China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China

An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste(MSW).This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission.As the categories of MSW are diverse considering their compositions,chemical reactions,and processing procedures,etc.,resulting in low efficiencies in MSW sorting using the traditional methods.Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode.This study for the first time applied MSWNet in MSW sorting,a ResNet-50 with transfer learning.The method of cyclical learning rate was taken to avoid blind finding,and tests were repeated until accidentally encountering a good value.Measures of visualization were also considered to make the MSWNet model more transparent and accountable.Results showed transfer learning enhanced the efficiency of training time(from 741 s to 598.5 s),and improved the accuracy of recognition performance(from 88.50%to 93.50%);MSWNet showed a better performance in MSW classsification in terms of sensitivity(93.50%),precision(93.40%),F1-score(93.40%),accuracy(93.50%)and AUC(92.00%).The findings of this study can be taken as a reference for building the model MSW classification by deep learning,quantifying a suitable learning rate,and changing the data from high dimensions to two dimensions.

关键词： Municipal solid waste sorting Deep residual network Transfer learning Cyclic learning rate Visualization

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