# A Waste Classification Method Based on a Multilayer Hybrid Convolution Neural Network
## 4 Experiments and Results Analysis
### Comparison with Related Literature  
With the same dataset, the proposed method is compared with other classification methods based on deep learning. The experimental results are listed in Table 6. [PERSON] et al. used the transfer learning method based on VGG19, which exploits pre-trained large-scale networks on a small amount of data using the transfer learning technology and achieved an 88.42% classification accuracy [20]. The work in [23] proposed a convolution neural network model constructed with the 50-layer residual network preprocessing (ResNet-50) as an extractor and used the support vector machine (SVM) to classify, achieving an 87% accuracy on a waste image set. The work in [27] proposed an improved model based on MobileNet and obtained a classification accuracy of 87.2%. After optimization and quantification, the accuracy reached 89.34%. The work in [30] took advantage of the classical deep learning models, trained a network with an Iception-ResNet model, and compared its classification performance with that of several convolution neural networks on a waste image dataset, finally achieving the best accuracy of 88.60%. [PERSON] et al. studied different types of neural networks and classified the waste images into four categories, among which the accuracies obtained by the KNN, SVM, and RF pre-training model methods were 88.0%, 80.0%, and 85.0%, respectively [32]. [PERSON] et al. used a Faster R-CNN [38] model with faster fine-tuning to classify the mixed waste images, and a 68.30% classification accuracy was achieved [39]. The work in [40] arranged the convolution neural network in parallel with various methods, and the best classification accuracy was 89.81%. In [41], the image processing used pre-trained deep convolutional networks with Single Shot Detectors (SSD) and MobileNetsV1. In [42], Vgg16, ResNet50, and Xception were pre-trained on ImageNet, and the highest classification rate was 88%. Most of the abovementioned methods used fine-tuning classical convolutional neural networks to classify waste images. Direct fine-tuning of classical convolutional neural networks has the disadvantage of having many network parameters and large computations. The proposed model achieved the best accuracy of 92.6% for waste classification. In addition, the proposed method had fewer parameters and a shorter iteration time. This means that the proposed method can provide a higher classification accuracy with lower complexity. Meanwhile, the classical convolution neural network based on fine-tuning is not suitable for the TrashNet dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **Year** & **Parameters** & **Accuracy** & **Gain** \\ \hline \multirow{8}{*}{TrashNet} & OscarNet (based on VGG19 pretrained) [20] & 2018 & 13,957,0240 & 88.42\% & 4.18\% \\  & Augmented data to train R-CNN [39] & 2017 & – & 68.30\% & 24.3\% \\  & Ref. [23] & 2019 & 22,515,078 & 87.00\% & 5.6\% \\  & Ref. [30] with Inception-ResNet & 2019 & 29,042,344 & 88.66\% & 3.94\% \\  & Ref. [33] with KNN & 2018 & – & 88.00\% & 4.6\% \\  & Ref. [33] with SVM & 2018 & – & 80.00\% & 12.6\% \\  & Ref. [33] with RF & 2018 & – & 85.00\% & 7.6\% \\  & Ref. [27] with MobileNet & 2018 & 42,000,000 & 89.34\% & 3.26\% \\  & Ref. [40] with CNN & 2018 & – & 89.81\% & 2.79\% \\  & Ref. [41] & 2020 & 29,000,000 & 88.42\% & 4.18\% \\  & Ref. [42] & 2020 & 20,875,247 & 88\% & 4.6\% \\  & Ours (MLH-CNN) & – & 1,709,926 & 92.60\% & – \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the results of the proposed method and other classification methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MLH-CNN** & **ResNet50** & **Vgg16** & **AlexNet** \\ \hline The lower right corner is blocked & 83.44\% & 67.59\% & 65.01\% & 45.92\% \\ The lower left corner is blocked & 84.75\% & 71.37\% & 69.38\% & 46.52\% \\ The top left corner is blocked & 83.01\% & 71.77\% & 62.82\% & 46.92\% \\ The top right corner is blocked & 79.96\% & 71.77\% & 70.58\% & 46.9\% \\ \hline \hline \end{tabular}
\end{table}