# Image Classification of Parcel Boxes under the Underground Logistics System Using CNN MobileNet
## 5 Conclusions and Future Work  
This paper introduces a ULS concept to reduce freight cars and handle freight volume using subways in metropolitan areas. After checking the point where the parcel boxes could be damaged based on a ULS, research was conducted using deep learning to detect whether the parcel boxes were damaged. Parcel boxes are susceptible to damage, which reduces product quality and negatively affects customer satisfaction. Therefore, inspection procedures for parcel boxes are needed to efficiently detect and classify the defects of parcel boxes before they are delivered to customers. As a way to solve this problem, we conducted an experiment to classify normal parcel boxes and damaged boxes using computerized methods under a ULS procedure. For image classification, Google Colaboratory notebook was used and 4882 data images were collected for the experiment. The experiment was performed using the MobileNet algorithm, which used less disk space and fewer parameters than other models. Regarding the architecture of MobileNet, multi-adds and parameters were presented as 460 and 2.65, respectively, for the fully convolutional layer which has an input 'F' of size 14 \(\times\) 14 \(\times\) 512 and a filter 'K' of size 3 \(\times\) 3 \(\times\) 512 \(\times\) 512. The multi-adds and parameters of the depthwise convolutional layer were 53 and 0.35, respectively. A MobileNet classification architecture of parcel boxes was trained and tested, with an accuracy, recall and specificity of 84.6%, 82% and 88.54%, respectively, for the testing set. Since the image dataset of parcel boxes is relatively new, there are no published \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Disk Space** & **Parameters** \\ \hline MobileNet & 12 MB & 3,017,284 \\ VGG16 & 502 MB & 117,237,524 \\ ResNet50 & 77 MB & 18,634,720 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of disk space and parameters between models. Figure 10: Comparison of the test results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Test Accuracy** & **Sensitivity** & **Specificity** & **Testing Time (s)** \\ \hline MobileNet & 84.6 & 82 & 88.5 & 14 \\ VGG16 & 88.3 & 86.3 & 89.7 & 98 \\ ResNet50 & 79.7 & 76.5 & 81.2 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of CNN image classification models. studies to compare our results to. Therefore, to validate the usefulness of the MobileNet algorithm, verification experiments were performed under the same conditions using other algorithms, VGG16 and ResNet50. As a result of these verification experiments, the accuracy of was 86.3% in the VGG16 algorithm, which was better than the other image classification models. However, the testing time of the VGG16 also took longer than MobileNet and ResNet50. Therefore, the results show that, although the accuracy of MobileNet is 84.6%, which is relatively lower than VGG16, its testing time was seven times faster than VGG16 algorithm. In conclusion, MobileNet has a relatively better performance than other image classification models with regard to test time. The contribution of this paper is twofold. Firstly, this study presents the concept of a ULS, and a search is currently being conducted to apply the proposed ULS to metropolitan areas. Secondly, this paper proposed a method to detect damaged parcel boxes using MobileNet, and this will be helpful for classification of damaged parcel boxes under ULS. Future studies that focus on a breakage-type classification of parcel boxes (contamination, leakage and damage) should be conducted. Another study could be conducted to track the location of detected parcel boxes in real time and to determine whether they are damaged or not. Conceptualization, M.K. and Y.K. ([PERSON]); methodology, Y.K. ([PERSON]); software, M.K. ; validation, Y.K. ([PERSON]); resources, J.K.; writing--original draft preparation, M.K. ; writing--review and editing, Y.K. ([PERSON]), Y.K. ([PERSON]) and J.K.; supervision, Y.K. ([PERSON]); project administration, Y.K.