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

Highlights

● MSWNet was proposed to classify municipal solid waste.

● Transfer learning could promote the performance of MSWNet.

● Cyclical learning rate was adopted to quickly tune hyperparameters.

Abstract

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.

Graphical abstract

Keywords

Municipal solid waste sorting / Deep residual network / Transfer learning / Cyclic learning rate / Visualization

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Acknowledgements

This work was financially supported by the China Scholarship Council (No. 202206260111) and the Research Project of Shanghai Chengtou Group Co., Ltd. (China) (CTKY-LGZX-2020-005-02).

Electronic Supplementary material

Supplementary material is available in the online version of this article athttps://doi.org/10.1007/s11783-023-1677-1 and is accessible for authorized users.

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