# Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine
## 4 Experimental Results and Discussions
### Dual Image-Based Classification with CNN Ensemble Models  
All previous experiments were conducted using only one cross-section of an object. Since the single view provides limited information about the object, the models trained in previous experiments produce erroneous predictions when classifying intentional fraud. To overcome this limitation and provide more details to the model, we used both the top view and the front view of input objects for training. We also constructed CNN ensemble models combining top view and front view classification results with a stacking ensemble scheme. The CNN models for the final ensemble were chosen based on their classification accuracy and size. From the previous experimental results, SqueezeNet and MobileNetV2 showed consistently good classification accuracy over other models. They also have a noticeable advantage in lightweight embedded platforms because the models have fewer parameters than other models. For this reason, we constructed CNN ensemble models using SqueezeNet and MobileNetV2. Table 6 shows the performance of four different CNN ensemble combinations from SqueezeNet and MobileNetV2. The results show that the best-fit ensemble model uses MobileNetV2 as the top view classifier and SqueezeNet as the front view classifier. The best-fit CNN ensemble model shows classification accuracy better than 95% for all the classes. Although the accuracy for the PET and the Can classes are slightly lower than the single CNN results in Table 5, we can consider this little performance degradation as a reasonable trade-off for the remarkable improvement in the accuracy for the fraud objects. To analyze what makes our proposed CNN ensemble model produce more accurate classifications of fraud objects than a single CNN, we applied kernel density estimation to the prediction results of the fraud objects. Figure 11 illustrates the kernel density estimation of the fraud object predictions in our experiments. Figure 11a,b show the results of MobileNetV2 trained with only the top view or the front view image, respectively, and Figure 11c shows the result of the final CNN ensemble model. Figure 11a,b indicate that \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Top view** & **Front view** & **\#Params** & **PET** & **Can** & \multicolumn{2}{c}{**Non-Target**} \\  & & & & & **Fraud** & **IAO** \\ \hline MobileNetV2 & SqueezeNet & 2.96 M & 97.73\% & 95.45\% & 97.14\% & 100.00\% \\ MobileNetV2 & MobileNetV2 & 4.46 M & 87.88\% & 95.45\% & 95.71\% & 100.00\% \\ SqueezeNet & SqueezeNet & 1.47 M & 90.15\% & 92.42\% & 98.57\% & 100.00\% \\ SqueezeNet & MobileNetV2 & 2.96 M & 87.12\% & 92.42\% & 81.43\% & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of CNN ensemble models trained with top view and front view data simultaneously, classifying objects into PET, Can, and non-target classes. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **\#Params** & **PET** & **Can** & \multicolumn{2}{c}{**Non-Target**} \\  & & & & **Fraud** & **IAO** \\ \hline DenseNet-121 & 7.0 M & 100.00\% & 99.24\% & 31.43\% & 100.00\% \\ SqueezeNet & 0.7 M & 100.00\% & 100.00\% & 34.29\% & 100.00\% \\ MobileNetV2 & 2.2 M & 93.18\% & 100.00\% & 61.43\% & 100.00\% \\ EfficientNet-B0 & 4.0 M & 83.33\% & 100.00\% & 70.00\% & 100.00\% \\ EfficientNet-B1 & 6.5 M & 68.94\% & 100.00\% & 68.57\% & 93.33\% \\ EfficientNet-B2 & 7.7 M & 93.18\% & 100.00\% & 47.14\% & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of CNNs trained with front view data, classifying objects into PET, Can, and Non-target classes. when MobileNetV2 incorrectly classifies a fraud object as PET or Can class after picking argmax, in most cases, prediction probabilities exceed the decision threshold of 0.5. This result leads to poor accuracy by finally misclassifying it as a legal target class. Figure 11c shows that our proposed CNN ensemble model also incorrectly identifies fraud objects as legal target objects after argmax.