Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches

Lin, Kunsen; Zhao, Youcai; Kuo, Jia‐Hong; Deng, Hao; Cui, Feifei; Zhang, Zilong; Zhang, Meilan; Zhao, Chunlong; Gao, Xiaoyang; Zhou, Tao; Wang, Tao

doi:10.1016/j.jclepro.2022.130943

Cited by 89 publications

(30 citation statements)

References 92 publications

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“…It is crucial to find a suitable way for the MSW treatment since it has a potential risk to human health and the ecological environment (Ding et al, 2021). Landfills, composting, and combustion are the common way for the MSW treatment, while the heterogeneity of MSW composition limits the application of various MSW treatment (Lin et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, great attention has been caught to applying deep learning for the waste classification related to computer version (CV) with the development of computer hardware (Nasri et al, 2020). Compared with traditional CV algorithms like scale-invariant feature transform (SIFT), supporting vector machine (SVM), and principal component (PCA) (Soleimani, 2016a,b;Lu and Chen, 2022), deep learning has the ability to automatically extract the representation and equips with more applicability, robustness, generalization, and scability (Lin et al, 2022;Mafakheri et al, 2022;Saad and Chen, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The essence of deep learning is the data-driven model, thus, measures like data augmentation and fine-tuned hyperparameters of deep learning structure could improve the performance (Lin et al, 2022). RecycleNet, combined with transfer learning and DenseNet-121, obtained 95% accuracy on the test dataset (Bircanoglu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Lin²

2022

Front. Environ. Sci.

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It is crucial to realize the municipal solid waste (MSW) classification in terms of its treatments and disposals. Deep learning used for the classification of residual waste and wet waste from MSW was considered as a promising method. While few studies reported using the method of deep learning with transfer learning to classify organic waste and residual waste. Thus, this study aims to discuss the effect of the transfer learning on the performance of different deep learning structures, VGGNet-16 and ResNet-50, for the classification of organic waste and residual waste, which were compared in terms of the training time, confusion matric, accuracy, precision, and recall. In addition, the algorithms of PCA and t-SNE were also adopted to compare the representation extracted from the last layer of various deep learning models. Results indicated that transfer learning could shorten the training time and the training time of various deep learning follows this order: VGGNet-16 (402 s) > VGGNet-16 with TL (272 s) > ResNet-50 (238 s) > ResNet-50 with TL (223 s). Compared with the method of PAC, waste representations were better separated from high dimension to low dimension by t-SNE. The values of organic waste in terms of F1 score follows this order: ResNet-50 with transfer learning (97.8%) > VGGNet-16 with transfer learning (97.1%) > VGGNet-16 (95.0%) > ResNet-50 (92.5%).Therefore, the best performance for the classification of organic and residual waste was ResNet-50 with transfer learning, followed by VGGNet-16 with transfer learning and VGGNet-16, and ResNet-50 in terms of accuracy, precision, recall, and F1 score.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Lin²

2022

Front. Environ. Sci.

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“…Intelligent recognition of waste categories is a prerequisite for sorting and recycling. Computer vision technology and deep learning technology can automatically detect and classify waste categories [ 5 , 6 ], providing technical support for waste sorting and recycling.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet

Feng

Yang

Chen

et al. 2022

IJERPH

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The main source of urban waste is the daily life activities of residents, and the waste sorting of residents’ waste is important for promoting economic recycling, reducing labor costs, and protecting the environment. However, most residents are unable to make accurate judgments about the categories of household waste, which severely limits the efficiency of waste sorting. We have designed an intelligent waste bin that enables automatic waste sorting and recycling, avoiding the extensive knowledge required for waste sorting. To ensure that the waste-classification model is high accuracy and works in real time, GECM-EfficientNet is proposed based on EfficientNet by streamlining the mobile inverted bottleneck convolution (MBConv) module, introducing the efficient channel attention (ECA) module and coordinate attention (CA) module, and transfer learning. The accuracy of GECM-EfficientNet reaches 94.54% and 94.23% on the self-built household waste dataset and TrashNet dataset, with parameters of only 1.23 M. The time of one recognition on the intelligent waste bin is only 146 ms, which satisfies the real-time classification requirement. Our method improves the computational efficiency of the waste-classification model and simplifies the hardware requirements, which contributes to the residents’ waste classification based on intelligent devices.

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“…Many barriers exist to the implementation of digital waste management systems, including a lack of policymakers’ knowledge as well as the deficiency of standards and strategic rules [ 6 ]. Hence, numerous analysts have greatly contributed to exploring the impact of diverse components on the waste management era and various models to anticipate waste generation [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

Ahmed

Mubarak

et al. 2022

IJERPH

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The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in garbage, and many urban areas are becoming increasingly difficult to manage. The advancement of technology allows researchers to transmit data from municipal bins using smart IoT (Internet of Things) devices. These bin data can contribute to a compelling analysis of waste management instead of depending on the historical dataset. Thus, this study proposes forecasting models comprising of 1D CNN (Convolutional Neural Networks) long short-term memory (LSTM), gated recurrent units (GRU) and bidirectional long short-term memory (Bi-LSTM) for time series prediction of public bins. The execution of the models is evaluated by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient determination (R2) and Root Mean Squared Error (RMSE). For different numbers of epochs, hidden layers, dense layers, and different units in hidden layers, the RSME values measured for 1D CNN, LSTM, GRU and Bi-LSTM models are 1.12, 1.57, 1.69 and 1.54, respectively. The best MAPE value is 1.855, which is found for the LSTM model. Therefore, our findings indicate that LSTM can be used for bin emptiness or fullness prediction for improved planning and management due to its proven resilience and increased forecast accuracy.

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Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches

Cited by 89 publications

References 92 publications

Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

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