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. 2022 Nov 30;19(23):15987.
doi: 10.3390/ijerph192315987.

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

Zhicheng Feng  1   2 , Jie Yang  1   3 , Lifang Chen  4 , Zhichao Chen  1   2 , Linhong Li  1   2
Affiliations

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

Zhicheng Feng et al. Int J Environ Res Public Health. .

Abstract

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.

Keywords: EfficientNet; artificial intelligence; image classification; sustainable development; waste sorting and recycling.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The intelligent waste bin mechanical modeling and simulation modeling are shown in (a) and (b) respectively, where they were created by SolidWorks. Subfigure (c) shows the physical construction of the intelligent waste bin, which is constructed by the camera, servos, bins, infrared sensor, baffle plate, and paddle plate.
Figure 2
Figure 2
Control circuit. It is built through the Raspberry Pi 4B, camera, servos, and infrared sensor.
Figure 3
Figure 3
MBConv module structure.
Figure 4
Figure 4
Improved EfficientNet structure.
Figure 5
Figure 5
Efficient channel attention module.
Figure 6
Figure 6
Coordinate attention module.
Figure 7
Figure 7
The results of the ablation experiment on the self-built dataset. Subfigure (a) shows the loss curve, where the loss value is calculated on the training dataset. Subfigure (b) shows the accuracy curve, where the accuracy is calculated on the test dataset.
Figure 8
Figure 8
The results of the model performance comparison on the self-built dataset. Subfigure (a) shows the loss curve, where the loss value is calculated on the training dataset. Subfigure (b) shows the accuracy curve, where the accuracy is calculated on the test dataset.
Figure 9
Figure 9
The results of the model performance comparison on the TrashNet dataset. Subfigure (a) shows the loss curve, where the loss value is calculated on the training dataset. Subfigure (b) shows the accuracy curve, where the accuracy is calculated on the test dataset.
Figure 10
Figure 10
Inference time on the Raspberry Pi 4B.
Figure 11
Figure 11
Confusion matrix of GECM-EfficientNet on the test dataset of the self-built dataset. The x-axis and y-axis represent the true and predicted categories of waste respectively, and the different categories are represented by numbers. Subfigure (a) shows the number of predictions for each waste category. Subfigure (b) is obtained by normalizing subfigure (a).
Figure 12
Figure 12
Test results for partial waste images, where the waste type, name and classification accuracy are shown.
See this image and copyright information in PMC

References

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