# Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine
## 5 Conclusions  
Current RVMs and previous studies on waste classification have suffered from limited collection resource scope, high system configuration cost, and the erroneous prediction of intentional frauds. To tackle these problems, we proposed a CNN ensemble model that combines two CNN models trained for different views of objects using a stacking ensemble scheme. To this end, we first investigated the classification performance of popular CNN models based on the image dataset acquired from our prototype RVM. From the results, we notice that the single image-based classification approach is fragile to intentional fraud objects. Therefore, we attempted to use both the top view and the front view of objects and construct the ensemble models with the classifiers having good accuracy for single image-based training tests and small model sizes. As a result, we finally set up the best-fit ensemble model that uses MobileNetV2 as the top view classifier and SqueezeNet as the front view classifier. The experimental results for the best-fit ensemble model derived in this study showed that our approach could produce classification accuracy higher than 95% for all target classes. In particular, our model significantly outperformed single image-based CNNs in classifying fraud objects. This improvement is due to two reasons. First, the dual image-based system provides more object information than the single image-based system and thus leads to better accuracy on target objects. Second, the confidence measure of intentional fraud objects is lower than the single image-based system, leading to better accuracy after passing the decision threshold layer. Furthermore, the proposed model presented an inference time of less than 70 ms on the NVIDIA Jetson platforms. This result also demonstrates that our model can achieve an execution performance comparable to a lightweight commercial RVM solution. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Platform** & **Jetson Nano** & **Jetson TX1** \\ \hline GPU & 128-core NVIDIA Maxwell & 256-core NVIDIA Maxwell \\  & Quad-Core ARM Cortex-A57 & Quad-Core ARM Cortex-A57 \\  & @1.42 GHz & @1.73 GHz \\ Memory & 4 GB 64-bit LPDDR4 & 4 GB 64-bit LPDDR4 \\ AI Performance & 472 GFLOPs & 1.0 TFLOPs \\ Size & 100 mm \(\times\) 80 mm \(\times\) 29 mm & 170 mm \(\times\) 170 mm \(\times\) 15 mm \\ Price & USD 99 & USD 480 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Specification of Jetson Nano and Jetson TX1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Platform** & **Mean** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \
\begin{tabular}{c} **99\% Confidence Interval** \\ **Lower Bound** \\ \end{tabular} &
\begin{tabular}{c} **Interval** \\ **Upper Bound** \\ \end{tabular} \\ \hline Jetson Nano & 67.25 & 19.54 & 65.79 & 68.72 \\ Jetson TX1 & 55.24 & 16.01 & 54.04 & 56.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The proposed model’s inference time on Jetson Nano and Jetson TX1. The unit of inference time is ms. The future work includes using more than two different views, such as the top, front, and backward views. The use of multiple views can classify the object regardless of the input direction. Conceptualization, T.Y. ; methodology, T.Y., S.L. and T.K. ; experiment and analysis, T.Y. ; supervision, T.K. ; funding acquisition, T.K. ; writing, T.Y., S.L. and T.K. All authors have read and agreed to the published version of the manuscript. This work was financially supported by Korea Ministry of Land, Infrastructure and Transport(MOLIT) as \"Innovative Talent Education Program for Smart City\". The dataset used in this paper is accessible on Github repository: [[https://github.com/taeyoungYoo/rvm-dataset](https://github.com/taeyoungYoo/rvm-dataset)]([https://github.com/taeyoungYoo/rvm-dataset](https://github.com/taeyoungYoo/rvm-dataset)) (accessed on 15 November 2021). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: